Transactive control and coordination framework and associated toolkit functions

ABSTRACT

Disclosed herein are representative embodiments of methods, apparatus, and systems for facilitating operation and control of a resource distribution system (such as a power grid). For example, embodiments of the disclosed technology can be used to improve the resiliency of a power grid and to allow for improved consumption of renewable resources. Further, certain implementations facilitate a degree of decentralized operations not available elsewhere.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application 61/737,726 filed on Dec. 14, 2012, and entitled “TRANSACTIVE CONTROL FRAMEWORK AND TOOLKIT FUNCTIONS”, which is hereby incorporated herein by reference.

ACKNOWLEDGMENT OF GOVERNMENT SUPPORT

This invention was made with government support under DE-0E0000190 awarded by the Department of Energy. The government has certain rights in the invention.

FIELD

This application relates generally to the field of power grid management and control.

SUMMARY

Disclosed below are representative embodiments of methods, apparatus, and systems for facilitating operation and control of a resource distribution system (such as a power grid). For example, embodiments of the disclosed technology can be used to improve the resiliency of a power grid and to allow for improved consumption of renewable resources. Further, certain implementations facilitate a degree of decentralized operations not available elsewhere.

“Transactive control and coordination” features market-like mechanisms for the selection of resources and demand-side assets in an electric power grid. The disclosed technology concerns new embodiments of transactive control and coordination. Such embodiments allow for transactive control and coordination where: (1) the system is implemented over large geographic areas; (2) the system is implemented across multiple grid regulation and/or business boundaries; (3) a large diversity of participating resources and loads are to be coordinated; and/or (4) the system desirably functions at multiple scales (e.g., both large areas of the transmission region and at individual devices).

Locations on the electric power grid that perform one or more of the disclosed techniques of are sometimes referred to herein as “transactive nodes.” Further, embodiments of the disclosed technology are described in terms of an “algorithmic framework,” where the highest-level responsibilities that are to be conducted at a transactive node are discussed. In certain embodiments, two functional blocks within the algorithmic framework allow for the further incorporation of (1) “toolkit resource functions” and/or (2) “toolkit load functions.” For example, depending on the unique features extant at a given transactive node (e.g., certain types of generation resources, inelastic electrical loads, other loads that might be responsive to a price-like signal in a demand-responsive way), one or more toolkit functions and their unique functionality may be incorporated. These toolkit functions can respectively modify the formulation of the price-like signal by the framework, or modify the amount of load that is to be generated or consumed by assets at this grid location. The functions can also advise the control of responsive assets.

Embodiments of the disclosed technology can be used to realize the fully distributed coordination of electrical power grids. In certain embodiments, such coordination can be accomplished by having nearest circuit neighbors exchange transactive signals. Desirably, these signals include not only price and quantity signals for an imminent time interval, but also predicted signals for future time intervals. In certain implementations, at least two subclasses of transactive signal are used—one price-like and the other representing power. The transactive signal that represents power (the TFS) is usefully aggregated where the power is also combined in a circuit and represents the power flow between circuit neighbors; a price-like signal (the TIS) may fairly represent costs of multiple resources and incentives if such costs are proportionately added where the resources are injected into and where the incentives occur in the electrical circuit.

In certain implementations, and in contrast to system utilizing explicit bilateral markets, some of the disclosed systems use planned energy consumption as the feedback.

Also disclosed herein are tools and techniques for computing distributed relative power flow. For example, a distributed relative power flow method is formulated for electrical power systems. In certain embodiments, a node is allowed to allocate its generation or load changes among the power flows with its neighbors without the global knowledge of the power system. Further, in some embodiments, decisions are made independently at distributed locations to respond to incentive signals from distributed transactive control. The impacts of these decisions on power flow are desirably predicted, which is presently challenging to do with conventional power flow formulations. In certain embodiments, parallel computation is an inherent feature of the disclosed formulation.

Conventional power flow solvers, usually located at a central location, rely on the global knowledge of the power system to predict the impacts of generation or load changes on the power flow. However, it is challenging to predict the power flow by using such solvers at distributed locations, where only information from neighbor nodes may be available. This is not the case with embodiments of the disclosed distributed relative power flow formulations.

Embodiments of the disclosed power flow formulation can be used in a variety of environments. For example, such implementations can be used as part of a “smart grid” system, which heavily relies on two-way communication and transactive control.

Decisions to respond to incentive signals from transactive control cause power flow changes, which can be predicted in parallel at distributed locations, without knowledge of the entire power system.

Details of exemplary non-limiting embodiments of the disclosed technology are disclosed and illustrated in the sections below. Any one or more of the features, aspects, and/or functions described in any of the sections below or above can be used alone or in any combination or sub-combination with one another.

Embodiments of the disclosed methods can be performed using computing hardware, such as a computer processor or an integrated circuit. For example, embodiments of the disclosed methods can be performed by software stored on one or more non-transitory computer-readable media (e.g., one or more optical media discs, volatile memory components (such as DRAM or SRAM), or nonvolatile memory or storage components (such as hard drives)). Such software can be executed on a single computer or on a networked computer (e.g., via the Internet, a wide-area network, a local-area network, a client-server network, a cloud-based network, or other such network). Embodiments of the disclosed methods can also be performed by specialized computing hardware (e.g., one or more application specific integrated circuits (“ASICs”) or programmable logic devices (such as field programmable gate arrays (“FPGAs”)) configured to perform any of the disclosed methods). Additionally, any intermediate or final result created or modified using any of the disclosed methods can be stored on a non-transitory storage medium (e.g., one or more optical media discs, volatile memory or storage components (such as DRAM or SRAM), or nonvolatile memory or storage components (such as hard drives)) and are considered to be within the scope of this disclosure. Furthermore, any of the software embodiments (comprising, for example, computer-executable instructions which when executed by a computer cause the computer to perform any of the disclosed methods), intermediate results, or final results created or modified by the disclosed methods can be transmitted, received, or accessed through a suitable communication means.

The foregoing and other objects, features, and advantages of the invention will become more apparent from the following detailed description, which proceeds with reference to the accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The application contains at least one drawing executed in color. Copies of this patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee.

FIG. 1 is a generalized example of a suitable computing hardware environment for a computing device with which several of the described embodiments can be implemented.

FIG. 2 is a block diagram illustrating the transactive control concept.

FIG. 3 is an illustration of the node-by-node changes to a transactive incentive signal as it flows from generation to end-use.

FIG. 4 illustrates the dynamics of an electric vehicle charging example of the disclosed technology.

FIG. 5 illustrates a simple topology for wind availability as will be used to illustrate an embodiment of the disclosed technology.

FIG. 6 is a representation of toolkit functions for bulk power resources

FIG. 7 is a graph showing the power generated at the transactive node represented by FIG. 5 over time.

FIG. 8 is a graph illustrating the unit costs of power for the current transactive control example.

FIG. 9 is a graph that presents the hourly resource costs for wind power according to a conventional approach versus a transactive control approach.

FIG. 10 is a graph that shows the cumulative cost comparison for a transactive control approach versus a conventional approach.

FIG. 11 is a graph illustrating an example transactive incentive signal as it is affected by a wind power resource.

FIG. 12 is a skeleton diagram of the algorithmic framework at a transactive node.

FIG. 13 is a block diagram illustrating the example timing model.

FIG. 14 is a diagram exemplifying the stacked component resource and incentive costs that compose a transactive signal.

FIG. 15 is another diagram showing an example skeleton model of a standard transactive node that emphasizes the relationship between an exemplary overall methodology and the toolkit functions.

FIG. 16 is a diagram illustrating one view of how multiscale intervals could be addressed by embodiments of the transactive system.

FIG. 17 is a simple view of the responsibilities of a transactive node.

FIG. 18 illustrates a basic transactive node model.

FIG. 19 illustrates the constraint function transactive node component.

FIG. 20 illustrates the load function transactive node component.

FIG. 21 is a graph showing conceptual responses of methods to variation of an incentive signal.

FIG. 22 illustrates a supply function node component.

FIG. 23 illustrates a general transactive node.

FIG. 24 is a flowchart illustrating an exemplary method for operating a transactive node according to certain embodiments of the disclosed technology.

FIG. 25 is another flowchart illustrating an exemplary method for operating a transactive node according to certain embodiments of the disclosed technology.

FIG. 26 is another flowchart illustrating an exemplary method for operating a transactive node according to certain embodiments of the disclosed technology.

FIG. 27 is another flowchart illustrating an exemplary method for selecting a specific toolkit function from among a library of such toolkit functions.

FIG. 28 illustrates the structure of numbered attributes at an exemplary transactive node.

FIG. 29 shows an example state diagram for a transactive node.

FIG. 30 is an exemplary connection state diagram that applies to transactive neighbors, system managers, assets, and local information.

FIG. 31 is a diagram that illustrates TIS and TFS generation being decoupled.

FIG. 32 is a diagram that illustrates TIS processing as may occur for some embodiments.

FIG. 33 is a diagram illustrating an example where a perpetual exchange of signals might become sustained between two transactive node neighbors

FIG. 34 is a graph showing weighting factors for a set of demonstration intervals (IST₀=0:00) using three different values of constant γ

FIG. 35 is a flowchart showing an example toolkit framework of functions and processes at a transactive node.

FIG. 36 is a flowchart illustrating an exemplary “receive transactive incentive signal” process.

FIG. 37 is a flowchart for an exemplary “calculate new transactive signal intervals” process.

FIG. 38 is a flowchart illustrating an exemplary “formulate TIS” process.

FIG. 39 is a flowchart of an exemplary “formulate TFS” process.

FIG. 40 is a flowchart of an exemplary “sum total predicted load” process.

FIG. 41 is a flowchart of an exemplary “calculate applicable toolkit load functions” process

FIG. 42 is a flowchart of an exemplary “send transactive signals” process.

FIG. 43 is a flowchart of an exemplary “calculate applicable toolkit resource and incentive functions” process.

FIG. 44 is a flowchart of an exemplary “control responsive asset systems” process.

FIG. 45 is a flowchart of an exemplary “sum total predicted resources” process.

FIG. 46 is a flowchart of an exemplary “control responsive resource” process.

FIG. 47 is a set of graphs showing predicted load {circumflex over (P)} compared to measured load P for an example function.

FIG. 48 is a set of graphs that show the linear least-squares error fit for each hour of the day, for day 4 given the measured data for an example function.

FIG. 49 is a set of graphs that show the linear least-squares error fit for each hour of the day, for day 12 given the measured data for an example function.

FIG. 50 is a set of graphs that show the linear least-squares error fit for each hour of the day, for day 14 given the measured data for an example function.

FIG. 51 is a set of graphs showing predicted load {circumflex over (P)} compared to measured load P for an example function.

FIG. 52 is a set of graphs that show the linear least-squares error fit for each hour of the day, for day 4 given the measured data for an example function.

FIG. 53 is a set of graphs that show the linear least-squares error fit for each hour of the day, for day 12 given the measured data for an example function.

FIG. 54 is a set of graphs that show the linear least-squares error fit for each hour of the day, for day 14 given the measured data for an example function.

FIG. 55 is a graph of power vs. wind speed for wind turbines for an example function.

FIG. 56 is a graph of a hypothetical supply stack.

FIG. 57 is a diagram showing a sample daily DowJones Mid-C hourly index.

FIG. 58 is a plot of examplary overall cost of energy for hydropower for each season for an example function.

FIG. 59 shows example graphs for DIST(TIS₀) and φ(TIS₀).

FIG. 60 is a graph showing a typical water heater power consumption during week and weekend days.

FIG. 61 is an example profile of P_(S)(t).

FIG. 62 is a plot of a winter profile of T_(OSP)(t) that uses the winter parameters.

FIG. 63 is a plot of a summer profile of T_(OSP)(t) that uses the summer parameters.

FIG. 64 is a graph of the predicted electrical power consumption for 1000 thermostatically controlled residential buildings where T_(o)=10° C.

FIG. 65 is a graph of the predicted electrical power consumption for 1000 thermostatically controlled residential buildings where T_(o)=0° C.

FIG. 66 is a graph of the predicted electrical power consumption for 1000 thermostatically controlled residential buildings where T_(o)=0° C.; ΔT_(DRSP)=−2° C. from 8:00 to 10:00 am.

FIG. 67 is a graph of the predicted electrical power consumption for 1000 thermostatically controlled residential buildings where T_(o)=0° C.; K_(DRP)=0.75 from 8:00 to 10:00 am.

FIG. 68 is a plot showing results of simulating MATLAB code with one response level.

FIG. 69 is another plot showing results of simulating MATLAB code with one response level.

FIG. 70 is another plot showing results of simulating MATLAB code with one response level.

FIG. 71 is another plot showing results of simulating MATLAB code with one response level.

FIG. 72 is another plot showing results of simulating MATLAB code with one response level.

FIG. 73 is a plot showing results of simulating MATLAB code with two response levels.

FIG. 74 is another plot showing results of simulating MATLAB code with two response levels.

FIG. 75 is another plot showing results of simulating MATLAB code with two response levels.

FIG. 76 is another plot showing results of simulating MATLAB code with two response levels.

FIG. 77 is another plot showing results of simulating MATLAB code with two response levels.

FIG. 78 is an example plot of a lighting load.

FIG. 79 is an example plot of a refrigerator load.

FIG. 80 is an example plot of a cooking range load.

FIG. 81 is an example plot of a dishwasher load.

FIG. 82 is an example plot of a clothes washer load.

FIG. 83 is an example plot of a clothes dryer load.

FIG. 84 is an example plot of a miscellaneous electric load.

FIG. 85 is a block diagram showing an example model of ramp up and ramp down periods.

FIG. 86 is a block diagram of an example block input/output function model.

FIG. 87 is a set of plots for DIST(TIS₀) and φ(b).

FIG. 88 is an illustration of TOU voltage control concurrent with shedding water heaters.

FIG. 89 is a series of plots that show possible scenarios for changes in generation during one interval.

FIG. 90 is an infrastructure cost control diagram.

FIG. 91 shows a graph illustrating the improvement of uninitialized infrastructure cost estimate for different a parameter selections assuming 5-minute update intervals.

FIG. 92 shows a graph illustrating the uninitialized correction of TIS over time for different a parameter selections assuming 5-minute update intervals.

FIG. 93 is a diagram of an exemplary block input/output function model.

FIG. 94 is a graph illustrating an example for one iteration at a given time.

FIG. 95 is a diagram that shows the specified strategy during a month.

FIG. 96 is a graph illustrating power operations concepts.

FIG. 97 is a diagram of an exemplary block input/output function model.

FIG. 98 is a first diagram illustrating an example power flow computation.

FIG. 99 is a second diagram illustrating an example power flow computation.

FIG. 100 is a third diagram illustrating an example power flow computation.

DETAILED DESCRIPTION 1 General Considerations

Disclosed below are representative embodiments of methods, apparatus, and systems for facilitating operation and control of a resource distribution system (such as a power grid). The disclosed methods, apparatus, and systems should not be construed as limiting in any way. Instead, the present disclosure is directed toward all novel and nonobvious features and aspects of the various disclosed embodiments, alone and in various combinations and subcombinations with one another. Furthermore, any one or more features or aspects of the disclosed embodiments can be used in various combinations and subcombinations with one another. The disclosed methods, apparatus, and systems are not limited to any specific aspect or feature or combination thereof, nor do the disclosed embodiments require that any one or more specific advantages be present or problems be solved.

Although the operations of some of the disclosed methods are described in a particular, sequential order for convenient presentation, it should be understood that this manner of description encompasses rearrangement, unless a particular ordering is required by specific language set forth below. For example, operations described sequentially may in some cases be rearranged or performed concurrently. Moreover, for the sake of simplicity, the attached figures may not show the various ways in which the disclosed methods can be used in conjunction with other methods. Additionally, the description sometimes uses terms like “determine” and “generate” to describe the disclosed methods. These terms are high-level abstractions of the actual operations that are performed. The actual operations that correspond to these terms may vary depending on the particular implementation and are readily discernible by one of ordinary skill in the art. Furthermore, as used herein, the term “and/or” means any one item or combination of items in the phrase.

Any of the disclosed methods can be implemented using computer-executable instructions stored on one or more computer-readable media (e.g., non-transitory computer-readable media, such as one or more optical media discs, volatile memory components (such as DRAM or SRAM), or nonvolatile memory components (such as hard drives)) and executed by a processor in a computing device (e.g., a computer, such as any commercially available computer). Any of the computer-executable instructions for implementing the disclosed techniques as well as any intermediate or final data created and used during implementation of the disclosed systems can be stored on one or more computer-readable media (e.g., non-transitory computer-readable media). The computer-executable instructions can be part of, for example, a dedicated software application or as part of a software agent's transport payload that is accessed or downloaded via a network (e.g., a local-area network, a wide-area network, a client-server network, or other such network).

Such software can be executed on a single computer (e.g., a computer embedded in or electrically coupled to a sensor, controller, or other device in the power grid) or in a network environment. For example, the software can be executed by a computer embedded in or communicatively coupled to a sensor for measuring electrical parameters of a power line or electrical device, a synchrophasor sensor, a smart meter, a control unit for a home or household appliance or system (e.g., an air-conditioning unit; heating unit; heating, ventilation, and air conditioning (“HVAC”) system; hot water heater; refrigerator; dish washer; washing machine; dryer; oven; microwave oven; pump; home lighting system; electrical charger; electric vehicle charger; home electrical system; or any other electrical system having variable performance states), a control unit for a distributed generator (e.g., photovoltaic arrays, wind turbines, or electric battery charging systems), a control unit for controlling the distribution or generation of power along the power grid (e.g., a transformer, switch, circuit breaker, generator, resource provider, or any other device on the power grid configured to perform a control action), and the like. Further, any of the control units can also include or receive information from one or more sensors. Any of the transactive nodes described herein can be formed by such sensors, meters, control units, and/or other such units.

For clarity, only certain selected aspects of the software-based embodiments are described. Other details that are well known in the art are omitted. For example, it should be understood that the software-based embodiments are not limited to any specific computer language or program. For instance, embodiments of the disclosed technology can be implemented by software written in C++, Java, Perl, JavaScript, Adobe Flash, Python, JINI, .NET, Lua or any other suitable programming language. Likewise, embodiments of the disclosed technology are not limited to any particular computer or type of hardware. Details of suitable computers and hardware are well known and need not be set forth in detail in this disclosure. Furthermore, any of the software-based embodiments (comprising, for example, computer-executable instructions which when executed by a computer cause the computer to perform any of the disclosed methods) can be uploaded, downloaded, or remotely accessed through a suitable communication means. Such suitable communication means include, for example, the Internet, the World Wide Web, an intranet, software applications, cable (including fiber optic cable), magnetic communications, electromagnetic communications (including RF, microwave, and infrared communications), electronic communications, or other such communication means.

The disclosed methods can also be implemented by specialized computing hardware that is configured to perform any of the disclosed methods. For example, the disclosed methods can be implemented by a computing device comprising an integrated circuit (e.g., an application specific integrated circuit (“ASIC”) or programmable logic device (“PLD”), such as a field programmable gate array (“FPGA”)). The integrated circuit or specialized computing hardware can be embedded in or directly coupled to a sensor, control unit, or other device in the power grid. For example, the integrated circuit can be embedded in or otherwise coupled to a synchrophasor sensor, smart meter, control unit for a home or household appliance or system, a control unit for a distributed generator, a control unit for controlling power distribution on the grid, or other such device.

FIG. 1 illustrates a generalized example of a suitable computing hardware environment 100 for a computing device with which several of the described embodiments can be implemented. For example, any of the transactive nodes disclosed herein can be implemented by a computing hardware environment, such computing environment 100. The computing environment 100 is not intended to suggest any limitation as to the scope of use or functionality of the disclosed technology, as the techniques and tools described herein can be implemented in diverse general-purpose or special-purpose environments that have computing hardware.

With reference to FIG. 1, the computing environment 100 includes at least one processing unit 110 and memory 120. In FIG. 1, this most basic configuration 130 is included within a dashed line. The processing unit 110 executes computer-executable instructions. In a multi-processing system, multiple processing units execute computer-executable instructions to increase processing power. The memory 120 may be volatile memory (e.g., registers, cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flash memory), or some combination of the two. The memory 120 stores software 180 for implementing one or more of the described techniques for operating or using the disclosed systems. For example, the memory 120 can store software 180 for implementing any of the disclosed techniques.

The computing environment can have additional features. For example, the computing environment 100 includes storage 140, one or more input devices 150, one or more output devices 160, and one or more communication connections 170. An interconnection mechanism (not shown) such as a bus, controller, or network interconnects the components of the computing environment 100. Typically, operating system software (not shown) provides an operating environment for other software executing in the computing environment 100, and coordinates activities of the components of the computing environment 100.

The storage 140 can be removable or non-removable, and includes magnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, or any other tangible storage medium which can be used to store information in a non-transitory manner and which can be accessed within the computing environment 100. The storage 140 can also store instructions for the software 180 implementing any of the described techniques, systems, or environments. The input device(s) 150 can be a touch input device such as a keyboard, mouse, touch screen, pen, or trackball, a voice input device, a scanning device, or another device that provides input to the computing environment 100. The output device(s) 160 can be a display, touch screen, printer, speaker, or another device that provides output from the computing environment 100.

The communication connection(s) 170 enable communication over a communication medium to another computing entity. The communication medium conveys information such as computer-executable instructions, an agent transport payload, or other data in a modulated data signal. A modulated data signal is a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media include wired or wireless techniques implemented with an electrical, optical, RF, infrared, acoustic, or other carrier.

The various methods, systems, and interfaces disclosed herein can be described in the general context of computer-executable instructions stored on one or more computer-readable media. Computer-readable media are any available media that can be accessed within or by a computing environment but do not encompass transitory signals or carrier waves. By way of example, and not limitation, with the computing environment 100, computer-readable media include tangible non-transitory computer-readable media, such as memory 120 and storage 140.

The various methods, systems, and interfaces disclosed herein can also be described in the general context of computer-executable instructions, such as those included in program modules, being executed in a computing environment on a target processor. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, and the like that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or split between program modules as desired in various embodiments.

Computer-executable instructions for program modules may be executed within a local or distributed computing environment. As noted, the disclosed technology is implemented at least part using a network of computing devices (e.g., any of the computing device examples described above). The network can be implemented at least in part as a Local Area Network (“LAN”) using wired networking (e.g., the Ethernet IEEE standard 802.3 or other appropriate standard) or wireless networking (e.g. one of the IEEE standards 802.11a, 802.11b, 802.11g, or 802.11n or other appropriate standard). Furthermore, at least part of the network can be the Internet or a similar public network.

1.1 Acronyms and Abbreviations

This disclosure sometimes makes reference to the following acronyms:

HVAC heating, ventilating and air conditioning IST interval start time LMP locational marginal price RMS root mean square TCS transactive coordination system TFS transactive feedback signal TIS transactive incentive signal

UTC Coordinated Universal Time 1.2 Terms

This disclosure will sometimes make reference to the following terms, whose non-limiting definitions are provided below. These definitions do not necessarily apply in all instances and may vary depending on the context.

-   advisory control signal A signal that is transmitted by a     transactive node to its local responsive asset systems advising     these systems to change their energy consumption or generation -   asset model A usually dynamic model of an asset system (e.g., a     population of electric water heaters) that can predict its change in     load or change in supply in light of an event (e.g., a curtailment     of the asset system). -   locational marginal price A unit price of energy that represents the     spatial and temporal price of the marginal supply resource. Today,     locational marginal price is calculated centrally. -   non-transactive energy Refers to energy that can be exchanged     between transactive nodes of a transactive coordination system and     entities that reside outside the boundaries of the transactive     coordination system -   relaxation criterion A criterion against which changes in subsequent     transactive signals are compared. If changes are significant based     on this criterion, then new transactive signals are calculated and     published. -   transactive coordination system A distributed system-of-systems in     which transactive nodes coordinate the balance between energy     resources and loads by communicating transactive signals -   toolkit function A function that is invoked by the transactive node     object model to represent the unique set of incentives, resources,     and loads that are managed at the transactive node. Includes two     subclasses—toolkit resource and incentive functions and toolkit load     functions. -   toolkit load function One type of a plurality of toolkit functions     that calculates load, change in elastic load, and control signals     for the specific demand-side assets at a transactive node -   toolkit resource and incentive function One type of a plurality of     toolkit functions that calculates incentive costs, supply energy,     and energy costs for the specific incentives and supply resources at     a transactive node. Includes toolkit resource functions and toolkit     incentive functions. -   transactive energy Energy that is exchanged between transactive     nodes of a transactive coordination system -   transactive feedback signal One of a plurality of subclasses of     transactive signals. Represents predicted aggregate power flow     between two neighboring transactive nodes. -   transactive incentive signal One of a plurality of subclasses of     transactive signal. Represents the delivered unit cost of energy at     a system location. -   transactive neighbors Adjacent transactive nodes that exchange     energy and are therefore obligated to exchange transactive signals     with one another. This term may be equivalently stated as     neighboring transactive nodes or circuit neighbors. -   transactive node A node that participates in a transactive     coordination system to send and receive transactive signals -   transactive node object model The formal state model that resides at     a transactive node and defines its behaviors, interactions, and     interfaces. This term usually refers to the common responsibilities     of transactive nodes that are interoperable, standardized. -   transactive signal A class of signal shared between transactive     neighbors

2 Introduction

This section introduces some of the basic concepts of the disclosed transactive control and coordination technology. FIG. 2 is a block diagram illustrating a general system 200 for implementing transactive control. The figure represents a simple electric power system topology 200 with power flowing from generation resources on the right through the components of the system to loads on the left.

At any point in the topology where one can affect the flow of power, operational objectives may be taken into account. In the transactive control technique of the disclosed technology, these objectives can be monetized and included in a signal referred to as the “transactive incentive signal” (TIS). If at a given point, one should reduce load below that point, then the monetization computations will result in altering (e.g., raising) the value of the TIS. If, on the other hand, it is beneficial to add load below that point, then the computations will alter (e.g., lower) the value of the TIS in the opposite direction. In other words, by using embodiments of the disclosed transactive system, one can represent operational objectives to responsive elements of the system and incentivize them to change their behavior in response to the monetized objectives. In FIG. 2, this is represented by the arrow from right-to-left labeled “operational objectives.”

The responsive elements of the system also play an active role through making information available about their planned consumption of electric power. This is represented by the arrow from left-to-right labeled “status and opportunities.” In embodiments of the disclosed technology, information about the future forecast of the plans for generation resources and constraints associated with the flow of power through the system interact with temporally aligned information about the planned behavior or loads or other responsive resources. Local storage systems are an example of another type of responsive resource that may be thought of as being a positive, neutral (not consuming), or negative load.

With this general background, the following additional features of the transactive control and coordination system will now be introduced.

-   -   Transactive Control: A single, integrated, smart grid incentive         signaling approach utilizing an economic signal as the primary         basis for communicating the desire to change the operational         state of responsive assets.     -   Transactive Incentive Signal (TIS): A representation of the         actual delivered cost of electric energy at a specific system         location (e.g., at a transactive node). Includes both the         current value and a forecast of future values. In certain         embodiments, the current incentive signal value refers to the         value for the imminent (or next-to-occur) interval.     -   Transactive Feedback Signal (TFS): A representation of the net         electric load at a specific system location (e.g., between         neighboring transactive nodes). Includes both the current value         and a forecast of future values. In certain embodiments, the         current value refers to the feedback signal value for the         imminent (or next-to-occur) interval.

2.1 What is a Transactive Control Node?

The basic operational unit of embodiments of the illustrated transactive control technique is the transactive control node. In certain implementations, the transactive control node responds to system conditions as represented by incoming Transactive Incentive Signals and Transactive Feedback Signals through (a) incorporation of local asset status and other local information; (b) decisions about behavior of local assets; and/or (c) updating both transactive incentive and feedback signals. Inputs are used by the node to compute incentive and feedback signals. Further, in some embodiments, each signal is a sequence of forecasts for a time-series, so inputs will also be sequences of future (forecast/planned) values

Transactive control nodes may be implemented any place in the power system topology, preferably where it is possible to affect the flow of power in the system. This is true in both the bulk power system and carries through into the distribution system down to the end-use level. For example, embodiments of the disclosed technology can be used in a large region of the power grid (e.g., a large interconnected region of the transmission grid, sometimes referred to as a transmission zone), a distribution utility service territory, or for any other sized region, area, or space (e.g., at the substation level, at the feeder level, at a building level, or even at the household level. Transactive control nodes may be implemented down to the level of individual devices. One may also implement transactive control nodes that manage a collection of devices as an aggregated responsive asset or asset system.

2.2 An End-to-End View

FIG. 3 is an illustration 300 of the node-by-node changes to a transactive incentive signal (TIS) as it flows from generation to end-use. In particular, FIG. 3 provides a high level end-to-end view of the flow of transactive incentive signals through a transactive control and coordination system. In the figure, the TIS begins at a generation resource with the TIS values representing the generation cost. To simplify the example, transmission costs are also included so that when the signal is received at the utility-level, it represents the full cost of power delivered to the utility.

At the utility level, the utility has the opportunity to introduce local information and operational objectives. For example, the utility may wish to avoid demand charges associated with peak loads. The financial impact of peak loads can be used in calculating TIS values to incentivize load shifting.

In the example, there are also renewable generation assets local to the utility. The utility may also incentivize consumption of energy from these assets through the TIS. On the right hand side of FIG. 3, one can see the TIS presented to responsive assets as an aggregation of the costs to delivery power to the end-uses including generation costs, constraints, and operational objectives.

Missing from this example is the transactive feedback signal representing the behavior of the responsive assets. A feature of certain embodiments of the transactive control technique is that this signal and the transactive incentive signal are both used at a transactive control node to make decisions about the behavior of responsive assets controlled at that node or to be incentivized by that node. This interaction between the TIS and TFS takes place based on the forecast of cost of power delivered and the behavior of responsive assets. Through this interaction, a form of closed loop control is achieved. The decision logic and algorithmic functions of the transactive control node are desirably constructed in such a manner as to have convergence and to avoid oscillation.

2.3 An End-to-End View Via an Illustrative Example

One can better understand this interaction between the TIS and TFS through a simple qualitative example. Consider the following scenario. On a distribution feeder, imagine a pole top transformer feeding three houses. Each home has an electric vehicle. For this example, assume that each of the vehicle owners will want to fast charge their vehicle. With the normal base load for the three houses, all three vehicles fast charging will overload the pole top transformer.

In this example, the pole top transformer is receiving a TIS from upstream (presumably from the substation) and a TFS from each of the houses. The TFS from each house includes information about the planned charging activity for the corresponding electric vehicle. The transformer desirably makes decisions about whether to change the value of the TIS based on the current and future load as represented by aggregating the TFS from each house. It also may take into account other information, such as the ambient air temperature, weather forecasts, operating history, and so forth.

The three electric vehicles in this example, EV1, EV2, and EV3, each have different charging strategies. EV1 is capable of flexible charging, meaning that the rate of charge can be varied. EV2 charges at any cost. EV3 is a bargain hunter and will schedule charging when cost is low.

For this example, assume the following: EV1 desires to charge at 5 PM, EV2 wishes to charge at 6 PM and EV3 wishes to charge at 7 PM. Assume as well that there is a typical diurnal load curve for the three houses seen in this example as the combined load at the transformer. The pole-top transformer has a load rating of 40 kW. As long as the load is below 40 kW, the service life of the transformer is not being degraded. If the load is above 40 kW, then the service life of the transformer is reduced depending on factors including the load, the duration of load above the 40-kW limit, ambient air temperature and possibly other factors. The operating principle for the transformer's update to the TIS is a computation in which the monetary impact of load is computed based on the forecasted duration above the limit and the other factors mentioned. This computation can be performed with information about the cost to replace the transformer, the rated service life, and if desired, economic factors such as the cost of money. The point is that the impact of overloading the transformer is monetized and the result used to change the forecast value of the TIS.

The electric vehicle smart chargers may then respond to the change in TIS value (e.g., increased for overloading) and adjust their plans accordingly. A back and forth exchange, a negotiation if you will, takes place through the exchange of TIS and TFS updates. When the negotiation settles, then the “agreed” solution to consumption should be stable barring other perturbations.

A key challenge in this negotiation is to avoid oscillation. The algorithms and decision logic for both the smart charger and the transformer desirably have appropriate damping factors to drive the negotiation to a stable, non-oscillatory result. In this simple example, a qualitative result is presented to illustrate the nature of the interaction.

FIG. 4 illustrates the dynamics of the electric vehicle charging example. As described above, EV1 forecasts that it will start charging at 5 pm (hour 17), EV2 forecasts that it will start charging at 6 pm (hour 18) and EV3 forecasts that it will start charging at 7 pm (hour 19). None of the EV smart chargers have knowledge of the plans of the other. Information is communicated via their forecasts sent to the pole-top transformer and the resulting changes in the forecast TIS value.

In the figure, the broad dashed line represents the forecast total load. Notice that between hours 16 and 17, it simply tracks the normal diurnal load pattern. When the charging plans of the EV's are revealed through the TFS sent to the pole-top tranformer's transactive control node the forecast total load remains below the transformer's load limit until the time that EV2 proposes to start charging. Note that, in this example, all vehicles are proposing a level-2 fast charge initially.

When EV2 proposes to begin charging at hour 18, the forecast total load goes above the load limit. The TIS correspondingly increases above the TIS that is associated with the normal diurnal load. EV3's proposal to begin charging at hour 19 pushes the forecast load even higher. If all three vehicles are level-2 charging, the load approaches 10 kW above the load limit. With the three proposed charging times revealed, the TIS is adjusted and the vehicles respond. For this example, the result is simplified by showing the final result. In practice several iterations would typically be used to achieve the final, stable result.

The final result, as illustrated in FIG. 4, shows that EV1 adapts its plans based on its flexible charging strategy. EV2 does not modify its plan. Remember this is the vehicle that will charge at any price. EV3, the bargain hunter, chooses to shift charging to a night time hour when prices are even lower than its original proposal to begin charging at hour 19. As seen in the figure, EV1's flexible charging strategy offsets EV2's charge at any price to maintain the total load just at the transformer's load limit.

This simple example illustrated the basic principle of the transactive control technique. The technique can be applied at any point in the power system and can coordinate monetized energy impacts and the behaviors of responsive loads where such devices and opportunities exist. Consider, for example, a battery storage system at a distribution substation. The associated transactive control node would be making decisions about whether to charge, discharge, or do nothing with the battery system based on the incoming TISs, the incoming TFSs, local conditions such as the state of the battery system, and updating the TIS and TFS it sends to neighboring transactive control nodes accordingly. Transactive control nodes can be deployed throughout the power system from generation resources, through the transmission system, and in the distribution system down to end uses. The technique can be applied within end use points including residential, commercial and industrial uses to manage the behavior of responsive systems and devices.

2.4 Extended Example

The example above showed the use of the transactive control technique at end-use points within a distribution system. In this section, a further example of the transactive control and coordination system is considered. This example further illustrates the use of the technique to use local responsive assets to help facilitate the integration of intermittent renewable energy resources.

In order to facilitate discussion of this example, first consider the formalization of the transactive control technique. This allows the use of standard way of referring to the functional elements of an implemented transactive control and coordination system.

For embodiments of the disclosed technology, consider a formal model of the functionality of transactive control nodes. A transactive control node object state model has been defined and is the basis for implementing a transactive node object model (TNOM). This approach is scalable, algorithmic and supports explicit consideration of interoperability through the formal specification of both the syntax and semantics of the transactive incentive signal and transactive feedback signal. The “responsibilities” of a transactive control node summarized earlier are formally represented in the object model.

For embodiments of the disclosed technology, a standardized approach to implementation is made possible through the design and implementation of a “toolkit.” The toolkit includes well-defined interfaces to utility responsive asset systems and simple, common algorithms for updating transactive signals and determining “control” signals to responsive asset systems.

In designing the toolkit, functions for resources and loads can be defined. The resource functions are primarily defined for the bulk power system and represent systems that supply power. At the utility level, functions associated with local resources or utility concerns such as avoiding demand charges are defined. Load functions can be defined that are associated with the different classes of loads or with local resources such as battery storage systems that may have load or resource behaviors (which are treated as negative loads.)

In embodiments of the disclosed technology, the resource functions include functions from a wide variety of categories. For example, in certain embodiments, the resource functions include one or more of:

-   -   1. Imported electrical energy         -   1.1. Non-transactive imported energy         -   1.2. Transactive imported energy     -   2. Renewable energy resource         -   2.1. Wind energy         -   2.2. Solar energy         -   2.3. Hydropower     -   3. Thermal generation     -   4. General infrastructure cost     -   5. System constraints         -   5.1. Transmission constraints         -   5.2. Equipment and line constraints     -   6. System energy losses         -   6.1. Transmission losses         -   6.2. Distribution losses         -   6.3. Device/component losses     -   7. Demand charges     -   8. Market impacts

In embodiments of the disclosed technology, the load functions include one or more functions from the following categories: (1) inelastic, (2) elastic with limited numbers of discrete events available, (3) elastic with daily events available, or (4) elastic with a continuum or near continuum of responses available. There can then exist a matrix of these four categories, with specific loads that fit into one or more of these categories. For example purposes only, the following is a list of example load functions that should not be construed as limiting in any manner. For instance, load functions can be created for a wide variety of assets or asset systems that that can be used in embodiments of the disclosed technology (e.g., for a residence, there may be functions for a variety of different assets and/or asset systems, such as responsive water heaters, thermostats, clothes dryers, web portals, in-home displays, or other such assets and asset systems).

-   -   1. Bulk inelastic load         -   1.1. Bulk commercial load         -   1.2. Bulk industrial load         -   1.3. Bulk residential load         -   1.4. Small wind generator negative load         -   1.5. small-scale distributed generator negative load         -   1.6. Small-scale solar generator negative load     -   2. General event-driven demand response         -   2.1. Commercial         -   2.2. Distribution system voltage control         -   2.3. Residential behavior             -   2.3.1. Portals     -   3. General time-of-use demand response         -   3.1. Battery storage         -   3.2. Commercial         -   3.3. Residential behavioral             -   3.3.1. Portals         -   3.4. Residential         -   3.5. Distribution system voltage control     -   4. General real-time continuum demand response         -   4.1. Battery storage         -   4.2. Commercial         -   4.3. Residential behavioral             -   4.3.1. Portals         -   4.4. Residential

It should be understood that in embodiments of the disclosed technology, a transactive node may host multiple toolkit functions, including any combination of multiple resource and incentive functions, multiple load functions, or combinations of both resource and incentive and load functions. For instance, the resource and/or incentive functions used at a transactive node will typically depend on the location of the transactive node in a power grid topology, and on the one or more resources and/or loads for which the transactive node is responsible. This ability to “mix and match” resource and incentive functions while still maintaining a common transactive signal communication structure gives embodiments of the disclosed technology wide flexibility and scalability for implementing a transactive control system.

2.4.1 An Example Using Wind Resources

For this example, consider the following general conditions and objectives: (a) the predicted transactive incentive signal increases when wind energy decreases and visa versa; (b) the transactive incentive signal is communicated and mixed between transactive nodes; and/or (c) assets respond to improve consumption of wind when wind energy is available or near where wind is available.

For purposes of this example, also consider the simple topology 500 illustrated in FIG. 5. In the left hand side of the figure, a transactive control node can be observed with two generation resources. The lower illustration in the figure represents conventional generation such as a coal fired power plant. The upper illustration represents wind turbines. On the right hand side of the figure, one can see a transactive control node with three types of assets: conventional resistive load in the form of a water heater, a distributed energy resource in the form of battery storage, and a distribution system voltage control system represented by the cartoon with wires and power poles. For this example, the two transactive control nodes are communicating with each other through the exchange of transactive incentive signals and transactive feedback signals. Note that the transactive control node on the left is associated with features of the bulk power system—bulk generation resources—while the transactive control and coordination system on the right is associated with assets in the distribution system.

Consider now the toolkit load functions associated with the resources shown in the left hand side of illustration 600 in FIG. 6, which shows representations of toolkit functions for bulk power resources. A graphical representation of these toolkit functions is also shown in FIG. 6.

For conventional generation, toolkit resource function #2 shown in FIG. 6, the function is a single point representing a fixed cost of production. The vertical access represents cost in $/MWh and the horizontal axis the power produced. For purposes of this example, assume that this resource operates at a fixed point ignoring for this example ramping and any other factors that would cause the power output to vary.

The other example of wind power, toolkit resource function #1, is more complicated. In this case, assume a cost of power that is inversely proportional to the power output of the system. Thus, when there is low wind and low production the cost per unit of power is high. On the other hand, when there is high wind and corresponding high power output the cost is low. It should be noted that there are many possible ways to construct the resource functions. The underlying question is how to assign cost—to monetize the activity of the resource asset. In embodiments of the disclosed technology, one should assign cost in a way that incentivizes desired outcomes. In this example, the resource function defined for the wind resource has lowest cost when there is an abundance of wind power thus incentivizing consumption of wind power when it is available. Another consideration when evaluating potential resource functions is that candidate resource functions for a given asset should ensure the same total cost over relatively long periods of time.

Having defined resource functions allows one to look at their behavior over time. FIG. 7 is a graph 700 that depicts the power generated at transactive node #1. Base generation is shown as constant at 10 MW. Wind generation varies from 10 MW for the first 30 hours dropping to zero (0) thereafter.

With this forecast of power production in mind, consider the forecast of cost of power from these two resources both with current approaches and with the transactive control approach using embodiments of the resources functions disclosed herein.

In this example, short-term power trading on spot or even day-ahead markets is ignored. In this case, the cost of power will be an aggregated value based on the fixed rate associated with each of the two resources. From the point of view of today's consumer, the cost of power is at a fixed rate—thus there is no incentive to change consumption behavior associated with the cost of power.

FIG. 8 is a graph 800 illustrating the unit costs of power for the current transactive control example. In this case, the base generation is still provided at a fixed cost as previously shown. The unit cost of wind power is at a relatively lower cost while the wind is blowing and rises when the wind dies—eventually becoming infinite when wind power is unavailable. The aggregate cost, that seen by consumers, is an average (possibly weighted) of the two representing the incentive to consume when wind is available at a cost below normal base generation cost and to not consume when wind is unavailable at a cost above normal base generation cost.

Embodiments of the disclosed technology provide a scheme that incentivizes the desired behavior—preferentially to consume wind power. But what about the long-term cost objective? Let us compare how costs accumulate over time. FIG. 9 is a graph 900 that presents a comparison of hourly resource costs with or without transactive control. Given the examples, the resource function for base generation, the hour cost for that resource is the same in either case. For the wind resource, however, the hourly cost is quite different. As the system and economics are currently formulated, the wind resource is only compensated when it is producing power. The rate is fixed and costs should be recovered based on an estimate over the long term of the percentage of time the resource will be available. This is represented by the line in FIG. 9 that starts at the hourly cost of 100 and then drops to zero (0) at hour 31. In contrast, the hourly cost for the wind resource is constant at 40 using transactive control. This is because during the period of time when wind power is available, loads are incentivized to consume via a lower cost (e.g., using the transactive incentive signal) and incentivized to not consume via a higher cost when wind is not available. The cost of wind production still should be recovered. So over the excess cost recovery when wind is not available (as compared to base generation cost) is used to make the wind producer whole resulting in an apparent fixed hourly cost.

Integrating the hourly costs allows one to check the long-term criteria—that costs should be the same over the long term for transactive versus the non-transactive approaches. FIG. 10 is a graph 1000 that shows this cumulative cost comparison and shows that the transactive control technique can be formulated in such a manner as to achieve this objective.

Now that the formulation of toolkit resource functions have been considered, example differences between conventional approaches and embodiments of the transactive approach can be summarized. For instance, the resource functions for generation assets of the disclosed technology create a transactive incentive signal as depicted in graph 1100 of FIG. 11. The dynamics of the signal are as described above in the discussion of unit costs.

Attention can be shifted to the consumption, or load, side of the computation. From a behavioral or responsiveness point of view, loads will be mixed. Some will be controllable; in other words, the loads will have the potential to respond to an incentive signal. Still further, in some instances, some loads will also be capable of acting as a load or a generation resource. For example, a battery system may have either behavior, and decisions about the battery may be made about when to charge, discharge, and/or at what rates. In this respect, a battery load may be highly responsive. For any given class of load assets, one may construct one or more load toolkit functions. These functions desirably take into account the load functions for other distribution system assets, and are discussed in more detail below.

Embodiments of the disclosed technology implement a distributed system for engaging responsive assets within the power system to manage constraints and support the integration of elastic energy resource (e.g., wind power and/or other intermittent renewable energy resources).

In particular implementations, the technique primarily uses two signals—the transactive incentive signal and the transactive feedback signal—representing the cost of power delivered to a given point in the system and the load at a given point in the system respectively. In particular embodiments, both signals are forward forecasts. The use of these representations reduces communications capacity requirements but relies on the development of algorithms for monetizing operational objectives. This was illustrated through a simple electric vehicle charging example and an extended example for wind power integration.

3 Exemplary Embodiments of the Disclosed Transactive Control Signals 3.1 Introduction

The transactive control and coordination system (TCS) of the disclosed technology can be implemented primarily using two classes of transactive signals: transactive incentive signals (TIS) and transactive feedback signals (TFS). These signals are exchanged between distributed system sites. The purpose of these signals is to coordinate supply and load in the near future, from a few minutes to several days out.

Some might compare the TCS with locational marginal pricing (LMP), in which energy prices are differentiated by time and by circuit location to address the economics of resource availability and to help mitigate transmission system congestion. A TCS shares certain goals with LMP. Like an LMP price signal, a TIS is a price-like signal that may represent the value of energy resources while taking into account the location, the time, transmission congestion, and transmission losses. Unlike an LMP signal, however, a transactive signal has been generalized to represent other additional impacts that can be monetized. Furthermore, a TCS facilitates fully distributed, not centralized, formulations of transactive signals. Because the calculations may be fully distributed, a TCS system is scalable throughout transmission systems, distribution systems, customer premises, and/or device levels.

An LMP represents the cost of the marginal energy resource and is therefore useful for coordinating the dispatch of energy resources. An implication is that dispatch decisions for supply-side or demand-side resources are based solely on comparison against the current marginal resource. By contrast, embodiments of the TIS are preferably formulated to represent energy cost as a function of time and location so that it may coordinate multiple supply-side and demand-side resources, not just the marginal ones. (This distinction is increasingly of interest as must-run renewable resources become a significant fraction of system resources. Economic dispatch and marginal energy price are currently based largely on fuel expenses. Renewable resources, which consume no fuel, displace fueled resources. Therefore, the marginal price, which is determined by the marginal fueled resource, incurs downward pressure. If the resulting marginal price is used to calculate revenues, then revenues also experience downward pressure, even though the must-run renewable resources may have generated relatively expensive energy.) The economic usefulness of many resources is determined during planning stages, not as they operate. Once the resource has been built, it should be called upon anytime it is useful, not only when it competes well with the current marginal resource.

A TCS and its transactive signals, in principle, may thereby unify some decision processes that are conventionally addressed separately or sequentially—the using the dispatch of must-run resources and economic dispatch, for example, or the testing of economic power flow against permissible constrained power flow.

While quantity of energy is most certainly used during the calculations of LMP signals, there is seldom a need for those signals to be communicated outside the location of the central solver. In embodiments of the disclosed technology, however, the TFS, which represents a quantity of power, accompanies the price-like TIS. For example, distributed formulations can be used with signals that represent both the paired price and the quantity of power for time intervals. In particular, transactive signals can enable the coordination of the TCS, where each transactive node has a responsibility to perform its share of what is presently a very centralized calculation. The standardization of a TCS and its transactive signals can permit new implementers to join a TCS.

Now that some general characteristics of a TCS have been introduced, largely through a comparison between TCS and LMP systems (see, e.g., Table 1), further details and qualities of the TCS will be introduced. For example, the sections below describe the component parts of a TCS, including its transactive signals, and how each of the two subclasses of transactive signal are influenced and formulated.

TABLE 1 Comparison Summary between LMP and TCS LMP TCS Calculation is performed centrally Calculation may be distributed Signal represents unit price of Signals preferably represent inclusive marginal resource unit cost of energy and quantity of energy Somewhat scalable to Very scalable, in principle, disaggregated regions of throughout generation, transmission, generation, transmission, maybe distribution, customer, and end-use into distribution devices Usually relevant only to May represent perspectives of any perspective of one single system and many system component owners operator Contractually engages large May engage many small, flexible blocks of firm resources resources and large blocks of firm resources alike through the normal course of energy pricing or through alternative and diverse incentive mechanisms May include forecasted future Includes forecasted future intervals intervals

3.2 An Example Transactive Coordination and Control System

An exemplary embodiment of the TCS may be understood by its components and their behaviors. In particular implementations, its principal components comprise one or more of the following:

-   -   transactive node—system sites that are active participants in a         TCS. A transactive node hosts a transactive node object model         and exchanges transactive signals with its transactive         neighbors.     -   transactive signal—comprises one or more subclasses of signals         that are exchanged by transactive nodes. For instance, in         particular implementations, the transactive signal comprises two         subclasses that include the TIS and TFS.     -   transactive node object model—the state model of the actions and         responsibilities that are managed by a transactive node     -   toolkit functions—one or more functions that may be called upon         by the transactive node object model to customize it for the         unique set of inelastic and elastic supply and demand-side         resources that are managed at a respective transactive node. The         functions can belong, for example, to a plurality of subclasses.         The subclasses can include, for instance, toolkit resource and         incentive functions and toolkit load functions.

3.3 Example Transactive Node

In embodiments of the disclosed technology, transactive nodes are points in the topology of a TCS. In particular embodiments, transactive nodes periodically exchange transactive signals with their neighbors (e.g., their nearest neighbors) with which they can exchange electrical energy. For instance, transactive signals are exchanged between neighboring transactive nodes that share an electrical conductor. (This is true in the sense that two transactive nodes that exchange power also communicate. The actual pathway and communication media between transactive nodes can vary from implementation to implementation.) The resulting interconnection topology can, in some embodiments, be hierarchical. Transactive nodes can be established at any hierarchical point in the topology (e.g., at any point of the utility-side topology, such as a sub-station, feeder, transformer, or the loke) or at any point of the load-side topology, a feeder, transformer, household control unit, electric vehicle charger, or any control unit at the household or other load control unit).

3.4 Example Transactive Signals

Transactive signals can be represented as a series of data. For instance, in particular implementations, the transactive signals are a series of triplets. Each triplet is comprised of a time interval, a value, and a confidence level that qualifies the value. In other implementations, the transactive signals comprise a series of value pairs, where each value pair comprises any combination of a time interval, a value, or a confidence level. In still other implementations, the transactive signals comprise one or more of a time interval, a value, and/or a confidence level. In particular implementations, there are two subclasses of transactive signals:

-   -   the TIS—a representation of preferably the delivered unit cost         of the energy that is stated in the corresponding TFS. There is         a TIS representation at each transactive node and for each time         interval.     -   the TFS—the power flowing between two transactive nodes during a         given time interval. The unit cost of the energy that is being         exchanged is the corresponding TIS of the given time interval         and for the given transactive node that supplies the energy.         There is a TFS representation for each transactive neighbor at         each transactive node and for each time interval.

The examples herein were simplified to address real power and real energy. However, the reader skilled in the art of electrical power will understand that the examples could be extended to refer to real energy (meaning the product of real power and elapsed time), reactive energy (meaning the product of reactive power and elapsed time), or both real and reactive energy components. That is, a TIS may separately or jointly monetize real energy, reactive energy, or both real and reactive energies, and a TFS may represent real, reactive, or both the real and reactive power components of the power flowing between two transactive nodes.

3.4.1 Predictive Signal Intervals

In particular embodiments, the transactive signals are forecasts. The forecasts refer to an imminent time interval (e.g., the time interval that will start next) and a number of additional future intervals thereafter. The future intervals are defined by their starting times and durations. Once stated, an interval remains fixed in time, and a future interval moves closer with the passing of time. The intervals in a transactive signal are successive in one particular embodiment of the disclosed technology (e.g., they do not overlap).

A subsequent transactive signal updates the values and confidence levels for many or all of the previous transactive signal's time intervals. New intervals may also be created to push the forecast even farther into the future.

In one particular embodiment of the disclosed technology, termed “the demonstration”, 56 successive intervals ranging in duration from 5 minutes to 1 day were elected. Refer, for instance, to Table 2. It should be understood, however, that any number of intervals of any duration can be used to implement embodiments of the disclosed technology. In Table 2, the term “IST_(n)” refers to the time at which the n^(th) interval begins—the interval start time. The durations of the thirteenth, thirty-third, fifty-first, and fifty-fifth interval may change from one transactive signal to the next; this was done in the illustrated embodiment to make sure that the intervals remain aligned with major 15-minute, 1-hour, 6-hour, and 1-day transitions.

The shortest interval could be any duration. For instance, the duration might be limited by the sum of the system's calculation and communication latencies. If the system were to use relatively short intervals (e.g., five minutes or less), it could respond to many dynamic issues, even area control errors, which are typically managed on 4-second intervals.

In one embodiment, intervals were defined with increasingly longer durations into the future because more distant future values may only be meaningfully and accurately forecasted in a statistical, averaged sense. For example, if one knows the accurate status of a thermostat and the building temperature that the thermostat manages, one may accurately predict quite precisely when this system will begin or end its current heating or cooling cycle. For tomorrow, however, one cannot predict precisely when each cycle will begin and end, but one can quite accurately predict the fraction of time that the system will be actively cooling or heating. (In other embodiments, longer intervals (such as over 1 hour) are avoided. It has been observed, for example, that intervals longer than 1 hour tend to destroy important boundaries that have been defined at the boundaries between hours. For example, some utility billing practices presently distinguish “heavy load hours” that occur from 6:00 a.m. to 10:00 p.m. Pacific.)

The 56 intervals used in the example embodiment discussed herein extend more than 3 days into the future, but could extend to any desired time period. The total number of intervals and durations of the longest intervals in the example embodiment were influenced by the desire to allow the system to be unattended for at least three days—the duration of a long holiday weekend.

TABLE 2 Example Intervals Duration No. Intervals Interval Start Times 5 minutes 12 IST₀, IST₀ + 0:05, . . . , IST₁₀ + 0:05 15 minutes 20 Round(IST₁₁ + 0:15)*, IST₁₂ + 0:15, . . . , IST₃₀ + 0:15 1 hour 18 Round(IST₃₁ + 1:00)*, IST₃₂ + 1:00, . . . , IST₄₈ + 1:00 6 hours 4 Round(IST₄₉ + 6:00)*, IST₅₀ + 6:00, . . . , IST₅₂ + 6:00 1 day 2 Round(IST₅₃ + 1:00:00)*, IST₅₄ + 1:00:00, IST₅₅ + 1:00:00 >3 days 56 intervals 57 interval start times (IST) *The function “Round” indicates rounding down to the next 15-minute, 1-hour, 6-hour, or 1-day interval start time. Times are indicated as dd:hh:mm (days, hours, and minutes).

In Table 2, the 57^(th) IST was used to define the end of the 56^(th) interval, which is the final interval in a transactive signal of the example embodiment.

Published future intervals remain valid and may be used, in principle, until they are overcome by time. This means that a transactive signal's Friday forecast for a Monday morning interval can be used even if the system fails to calculate any new transactive signals through the weekend. In this capability, the system is resilient to temporary failures of individual system components. If, however, a part of the system fails, the signals that had been predicted much earlier become increasingly dated and inaccurate. The system also loses its ability to recognize and respond to change while new signals are absent. Also, because later intervals have longer duration, signal dynamics diminish as the system relies on progressively longer prior predictions. In one embodiment, the confidence attribute is degraded (e.g., indicates diminished confidence) over time as signals become stale, unupdated.

Although any suitable time standard can be used, embodiments of the disclosed technology use the Coordinated Universal Time (UTC) standard (ISO/IEC 2004). The UTC can be used, for example, to enforce a consistent and standardized representation of time across time zones. UTC times are unchanged across time zones and across transitions into and out of daylight savings periods. In certain embodiments, and in order to avoid problems with aligning time zones ad contractual obligations that may exist, the use of intervals longer than one hour is avoided.

3.4.2 Confidence Attribute of a Transactive Signal

In some embodiments, transactive signals also include a confidence attribute that is specified to qualify the values in the transactive signals. In particular implementations, the confidence attribute estimates the relative positive root-mean-square (RMS) accuracy of each value that is published in a transactive signal. In many cases, this interpretation is quite naturally incorporated. For example, forecasts for renewable energy resources are already qualified in a way comparable to an RMS error.

Some events or conditions are not as naturally represented using the metric relative RMS error. For example, one might have diminished confidence if a signal has been delayed or if some component information to be used in a calculation has become stale. Other examples might include startup conditions while only limited information has been received, suspect status of computational equipment that hosts a calculation, or calculated values that are simply outside a normally accepted range for unknown reasons. Nevertheless, these conditions can be functionally represented by relative RMS error.

The recipient of a value that is accompanied by a high relative RMS error may use such information in many ways. The local practices and policies may differ at each transactive node. The possible responses include, for example, the publication of error or warning flags, performing alternative calculations that are more conservative, resorting to safe default values, using statistical algorithms that optimize outcomes or minimize risk, or no action at all.

3.4.3 Transactive Incentive Signal

In particular embodiments, a transactive node has one TIS for any given time interval and any given calculation result. No differentiation of TIS value is allowed across a transactive node. If for any reason electrical energy should be valued differently across a transactive node, the transactive node should be divided into more than one node at the feature that causes different valuation.

In one particular implementation, the TIS is calculated by summing the incurred costs and dividing the sum by the energy to which the costs refer. The total energy may be thought of as either entire load (including exported energy), or as the entire supply (including imported energy), at the transactive node. The transactive node can assume that total supply is equal to total load. It has been found that it is more natural to work from the supply side during the formulation of TIS. It is the costs of the various mixes of supply resources that directly affect the TIS.

The input parameters of the TIS formula in Table 3 create a useful interoperability boundary. The parameters represent various costs (“C”) and power (“P”), where the subscripts refer to terms for energy (“E”), generation (“G”), capacity (“C”), infrastructure (“I”), or other (“O”). Further, subscript n is the interval number and Δt_(n) is that interval's duration. Members of a TCS may be invited to generate their own functional algorithms that in turn influence the TIS by simply designing algorithms that assign values to these various parameters. The parameters are distinguished by their units. Implementers may select and use the parameters that most naturally represent the forecasted cost impacts. It should be understood that these parameters are not limiting or even required for a particular component. In certain embodiments of the disclosed technology, the functions that generate these parameters are called toolkit resource and incentive functions. Resource functions model energy supply resources. Incentive functions affect the TIS, but they do not represent any energy resource. Example resource and incentive functions are described in more detail below, including Appendices B and C.

TABLE 3 Example formula by which the TIS is to be updated ${{TIS}_{n} = {\frac{{\sum\limits_{a = 1}^{A}\; {C_{E,a,n} \cdot {\hat{P}}_{G,a,n} \cdot {\Delta t}_{n}}} + {\sum\limits_{b = 1}^{B}\; {C_{C,b,n} \cdot {\hat{P}}_{C,b,n}}} + {\sum\limits_{d = 1}^{D}\; C_{O,d,n}}}{\sum\limits_{a = 1}^{A}\; {{\hat{P}}_{G,a,n} \cdot {\Delta t}_{n}}} + {\sum\limits_{c = 1}^{C}\; C_{{os},c}}}},$ Or ${TIS} = {\left( \frac{{{energy}\mspace{14mu} {cost}} + {{capacity}\mspace{14mu} {cost}} + {{other}\mspace{14mu} {costs}}}{{energy}\mspace{14mu} {resources}} \right) + {{offset}\mspace{14mu} {costs}}}$

In other embodiments, infrastructure costs are among the numerator terms. However, in such embodiments, an undesirable inverse relationship between TIS and total power demand may result. In Table 3, infrastructure costs can be included among the “offset costs”.

3.4.4 Transactive Feedback Signal

The TFS is calculated readily for a radial distribution circuit branch. The transactive node on a radial distribution branch simply sums its predicted inelastic and elastic loads. The upstream transactive node is the only resource available to supply the load at this system location, so the TFS is identical to the predicted load for the branch.

The TFS is not as easily predicted between transactive nodes that are not on a radial distribution branch and have more than one transactive neighbor. Their network system connections may be meshed. Desirably, power flow is allocated among multiple TFS in a way that would be fully consistent with a proper power flow calculation.

In a fully deployed TCS, economic dispatch decisions would be made at each transactive node to balance load. To the degree that energy can be imported from the transactive node's neighbors, the neighbors' energy competes with local resources. Any mismatch is desirably allocated among the TFSs.

In certain embodiments, each member of a pair of transactive neighbors estimates a TFS for the interface that they share. (The general case of meshed networks and bidirectional power flow desirably uses each transactive neighbor to publish and receive paired cost (TIS) and quantity (TFS) signals.) The convergence of the two estimates is a metric that can be used to determine whether the two neighbors have concluded their negotiated solution or not.

3.5 Transactive Node Object Model

In certain embodiments, the formal model of the transactive node class and its behavior has been specified by the transactive node object model.

3.5.1 Algorithmic Framework

An example model of the algorithmic responsibilities of a transactive node is introduced below in Appendix B. The details of this model can be used to implement exemplary transactive nodes (e.g., using Standard ISO/IEC 18012 (ISO/IEC 2004) or using a unified object-oriented modeling language such as UML-2 (OMG 2013)). The algorithmic framework has proven to be applicable across many different types of transactive nodes.

FIG. 12 is a skeleton diagram 1200 of the algorithmic framework at a transactive node. The diagram addresses two main objectives: First, it provides that the TIS may be calculated. Second, it provides that the TFS may be calculated.

A particular implementation of the function “3. Formulate TIS” is disclosed in Appendix B. This function receives information about intervals, costs of various resources and incentives, and the sum of imported and generated energy to which the cost information is relevant.

The model states that both the input information and resulting TIS values are stored in a data buffer. These buffer contents may be mined for data by those who have permission to do so. But the greater importance of the buffered data is that such stored information makes the system resilient to imperfect communications: the input values from a prior series of forecast intervals remain this transactive node's best prediction of the input interval values until updated information can be received. This is especially useful when the information is delayed or when a communication link becomes temporarily severed.

The impacts of energy supply and incentives (or disincentives) at a given transactive node are received through toolkit resource and incentive functions, a modular library of functions that model the costs and energy supplied by energy resources and other cost incentives or disincentives at a given transactive node. An example implementation of the function “8. Calculate Applicable Toolkit Resources and Incentives” (near the top center of FIG. 12) is disclosed in Appendix B. In certain embodiments, these toolkit functions are not themselves inside the algorithmic framework, but they inject their influences into the updating of the TIS via a standardized set of parameters.

A particular implementation of the function “4. Formulate TFS” (at the bottom right of FIG. 12) is disclosed in Appendix B. The objective of this algorithmic framework function is to forecast the flow of energy between it and its transactive neighbors. It therefore receives information about the set of future intervals. It also receives information about forecasted supply and load so that the balance may be allocated to the TFS between this transactive node and its transactive neighbors.

In certain embodiments, the load forecast has two threads. The first forecasts the inelastic load. This is the base case that is unaffected by the TIS. The second thread is the elastic load—the change in load that may be attributed to the TIS and events that are generated in light of the TIS. The separation of these threads is practical and it helps measure and verify system responses. The sum of the inelastic and elastic load forecast components accurately forecasts the actual load.

TABLE 4 Formula for total load used for TFS Total load = Inelastic load + Change in elastic load

The model of a single asset system may forecast both inelastic and elastic load components. For example, the thermostatic building asset model forecasts both its normal building load and the changes in load caused by temperature setback events. In certain embodiments of the disclosed technology, a single feeder model forecasted bulk inelastic load that in effect included many inelastic components of responsive assets. Provided that the components are properly summed for the given transactive node and not double-counted, it will not matter that the thermostat model did not model its own inelastic load component.

More information about the toolkit resource and incentive and toolkit load functions are discussed below as well as in Appendices B and C.

3.5.2 Signal Timing

In certain embodiments, the transactive node object model includes functionality and attributes that control the times at which transactive signals are transmitted to transactive neighbors. An exemplary timing model is discussed in this section, but is not to be construed as limiting, as any number of intervals having other durations can be used. The example timing model was designed to allow propagation of information about disturbances (e.g., of the electric transmission grid) across the TCS system while reducing unfruitful chatter and calculations. As noted, the example timing model is not necessarily one that should be standardized or used in implementations of the systems.

A transactive node should normally not publish transactive signals for which any interval starting time has already passed. This expectation creates a useful framework for the calibration of system clocks. The error between clocks at different system locations should desirably be small compared to the shortest intervals—5 minutes for the example timing model. Tight tolerances are, in principle, achievable for transactive nodes that are internet connected.

In the example timing model, each transactive node, at the beginning of a 5-minute interval, publishes transactive signals that address the interval that begins 5 minutes from now and into the future.

Various timers were implemented to avoid unnecessary chatter. One timer begins when a transactive signal is received. Another timer begins after a transactive signal is transmitted. No transactive signal of the same type may be transmitted again until after these timers expire. FIG. 13 is a block diagram 1300 illustrating the example timing model.

In one embodiment, the time model is event-based. For example, the timing model can be adapted to become more responsive to status or condition events and less reliant upon clock-based events (e.g., hold-down timers, interval timers). New signals and additional calculations can be generated only after significant changes occur to schedules and forecasts, either locally or at remote system locations. As long as forecasts remain accurate, the system should be unperturbed.

Further, sets of prediction intervals that are nested rather than sequential can be used. That is, an understanding that the next 5 minutes are a subset of an hour-long interval that is a subset of the day that is a subset of a month, and so on, can be adopted.

Still further, in some instances, a relaxation criterion against which forecast changes may be compared can be used. The criterion can state a weighting of errors for each interval. For example, if the sum of the errors exceeds the overall threshold for a transactive signal, then the signal is updated and republished; otherwise, no signals should be transmitted because the changes are deemed to be insignificant. This criterion can be used in an event-based model wherein imminent and future intervals are rapidly iterated (e.g., on an asynchronous basis) until they resolve according to this criterion.

3.6 Transactive Data Collection Layer

In some embodiments, a transactive data collection system layer is also defined and used in implementations of the transactive nodes. For example, this system layer automatically retrieves toolkit function outputs from resource, incentive, and toolkit load functions; gathers resulting TIS and TFS signals that are generated at each node from its toolkit function inputs; and records various system management events and statuses. Because the system is distributed both in time and space, it is desirable to keep track of data provenance, including locations of nodes from which the data originates, times at which signals are generated, and time intervals to which predictive signals refer.

One advantage of a TCS is that the transactive signals, while revealing an aggregated cost and quantity of energy, do not necessarily reveal any sensitive or private data. The model used to store and collect information about local resources and loads at a transactive node can be useful, but such information would normally be shared only with the owner of a set of transactive nodes, who is entitled to receive such privileged information. Desirably, little or no sensitive information is shared by neighboring transactive nodes.

“Non-transactive” data can also be defined and collected. Non-transactive data is factual data that is collected from system meters and which can be used during analysis to assess the success with which the predictive TCS has influenced system loads and its consumption of various energy resources. Non-transactive data can also include weather data at each distributed site.

3.7 Influences on the TIS

This section addresses the formulation and interpretation of the TIS.

3.7.1 The TIS is an Aggregate of Multiple Resource and Incentive Costs

In some embodiments, while each TIS states a value for each future interval, each said value may be composed of a plurality of various resource and incentive cost components. This concept is demonstrated by diagram 1400 in FIG. 14, which shows multiple stacked component costs, the sum of which is the published TIS value. The biggest cost component, in this example, is the unit cost of the energy that is received by this transactive node from its transactive neighbors (the transactive component). The remaining components are ranked as the cost of infrastructure and the unit costs of wind, hydroelectric, and fossil-fueled resources.

Observe that influences are inherited from neighboring transactive nodes that supply this transactive node. For example, if 8% of a TIS value is from the costs of fossil energy resources, and if this transactive node is supplied another 10% of its resources by a neighbor for which 10% of this neighbor's TIS value is from fossil resources, then the total impact of fossil energy on the TIS at this transactive node would be 8%+10%×10%=9%. Therefore, one can look to propagated resource mixes one, two, or even more neighbors distant to accurately assess the resource supply mix at this transactive node.

3.7.2 TIS Calibration Measurements Identified

As discussed, in certain embodiments, delivered cost of energy is used as the metric for TIS magnitude. This metric is useful because (1) it provides a straight path to using the signal for revenue, if other implementations choose to do so, and (2) comparable calibration standards exist at some locations within a TCS for this metric.

In a distributed system, checks and balances are desirable to make sure that the TIS, which is collaboratively formulated, is meaningful and fair. The first step toward accomplishing this was to establish a common semantic understanding of the TIS as, for one embodiment, the delivered cost of energy at a location. The second step is the comparison of the TIS and its components against comparable calibration standards. For example, existing and historical contracts define the average unit cost of energy among many suppliers and recipients of electrical energy. Distribution utilities can accurately state how much they paid for a unit of energy during the past year. Therefore, the TIS and any other valid representation of the delivered cost of energy at a system location should be comparable over long periods of time.

3.7.3 Resource Toolkit Functions

Adequate energy resources are desirably received into or dispatched at a transactive node to balance system load. The mix of dispatched energy resources can be determined in a distributed manner (though it is also possible to use a central determination for smaller scale implementations).

In certain embodiments, resource toolkit functions from a library of functions are the functions that calculate the quantity of energy and its cost impacts toward the formulation of the TIS at a transactive node. The resource toolkit functions can reside at any of the transactive nodes (e.g., transmission zone nodes, which each represent large regions of a region's generation and transmission systems). One or more of the following functions can be used to represent groups of (or individual) energy resources:

-   -   Non-transactive energy function—represents energy imported into         the system from entities that are not transactive nodes.     -   Transactive energy function—represents energy imported from a         neighboring transactive node.     -   Wind energy function—represents energy from wind farms in this         transactive node.     -   Hydropower generation energy function—represents energy from         hydropower at this transactive node.     -   Fossil generation energy function—represents energy from fossil         (more generally, “thermal”) resources at this transactive node.     -   Solar energy resource function—represents energy from solar         resources at this transactive node.

3.7.4 Incentive Toolkit Functions

Incentive functions are similar to resource functions, but they are not tied to energy supply. One or more of the following exemplary incentive functions can be used in implementations of the disclosed technology:

-   -   Transmission congestion management function—if the power flowing         through electricity transmission lines between two transactive         nodes ever approaches the capacity limit on the transmission         lines, this function adds cost disincentives to the downstream         transactive node to reduce load on the line.     -   Cost of general infrastructure function—a cost that is amortized         over time to represent the cost impacts of built infrastructure         that has not otherwise been captured in the system. The offset         from this function calibrates the TIS over time, pulling it         gradually toward a reasonable TIS at each transactive node.     -   Demand charges function—this is an incentive toolkit function         that can be applied at utility-site transactive nodes. Wholesale         electricity suppliers charge their utility customers according         to quite complex cost structures. This function attempts to         represent the cost impacts of demand charges and, to a lesser         degree, time-of-use charges. Functions have been drafted to         represent the cost structures of, for example, regional power         administrations.

3.8 Influences on the TFS

A TFS represents the power flowing between a transactive node and its transactive neighbor during the imminent and future intervals. The majority of the power flow is usually inelastic: it is unaffected by the predicted unit cost of the energy—the TIS. If the transactive node hosts responsive asset systems, these systems might observe the TIS and change their forecast of how much energy they will consume during a future interval—they are elastic. The transactive node state model keeps track of the changes in load that are anticipated from these elastic asset systems.

Responsive asset systems that curtail load reduce load at a transactive node and therefore tend to reduce the energy that is generated at or imported into the transactive node. Demand-side generators have the same impact when they generate energy and displace load at the transactive node.

Even more useful are responsive asset systems that can increase their energy consumption (or equivalently, reduce their demand-side generation). These asset systems thereby increase system load at their transactive nodes and increase the energy that is either generated at or imported into the transactive node. This response is increasingly useful in power grids that experience excessive generation, as now occurs in regions that have high wind-power penetration.

3.8.1 TFS Calibration Measurements Identified

A straightforward comparison standard exists for TFS values at many system locations. Because the TFS represents forecasted power flow, the accuracy of the forecasted power-flow values in a TFS may be compared against actual metered power at that point in the power grid. For example, the electricity supplied to a distribution by its electricity supplier is accurately metered.

3.8.2 Inelastic Load Prediction Functions

Inelastic load functions forecast baseline load that is unaffected by the TIS. Inelastic load functions can be defined for each residential, commercial, and industrial load type. The load from these models can be scaled by the numbers of each customer type. Alternatively, a parametric model can be used that can be trained by historical data. The model appears to perform similarly for all of the different load types. The forecast model creates a correlation to forecasted weather information—including at least ambient temperature. If available, the model can also incorporate recent measurement data to improve the forecast.

3.8.3 Elastic Load Functions

Elastic toolkit load functions in conjunction with asset models model how responsive asset systems are influenced by the TIS. In certain embodiments, these functions have two principal responsibilities: First, the toolkit load function predicts when events may occur and how long they will last. Second, an asset model forecasts the change in load that will occur during an event for the given asset system.

Elastic toolkit load functions can be categorized as follows based on the nature of their forecasted events:

-   -   Event-driven—several events may be called each month. The         principal challenge is to allocate a limited number of allowed         yearly, monthly, and daily events (e.g., curtailment events)         based on the forecasted TIS. Additional restrictions may apply         to the minimum and maximum durations of the events for a given         asset system.     -   Daily events (sometimes referred to herein as “time-of-use”         events)—events are expected to occur almost daily. The events         might be specified differently for weekdays, weekend days, and         holidays. The principal challenge is to place an event at the         best time of day based on the TIS. Additional restrictions may         apply to the minimum and maximum durations of the events for         each day type.     -   Continuous—in some embodiments, dynamic responses are being made         every interval. The challenge is not so much to specify events         as to state a functional relationship between each TIS value and         a system response.

An asset model then models the change in load during the above event types. It has been found that many possible pairings exist between event types and asset model types. For example, a water heater asset model may be used with either event-driven or daily event types. In principle, water heaters could be manufactured to have continuous responses. By way of example, one or more of the following exemplary asset models can be applied in an implementation of the disclosed technology:

-   -   water heater population—for instance, the population of         residential 40-gallon water heaters controlled by in-line         switches (e.g., demand-response units). (Models for other sizes         of water heater can also be used.) After the timing of events         has been predicted, the challenge is to predict the power and         energy that will be curtailed by the systems response.     -   thermostatic space conditioning with temperature setback—in one         implementation, a first-order thermal model of a building is         simulated. The model is scaled by numbers of building types and         their thermal properties, parameters which are desirably         configured by the implementer of this elastic toolkit load         function. Dynamic inputs include ambient temperature, solar         insolation, and modeled target interior temperatures that         represent occupancy temperature settings. During events, the         modeled target temperature is raised or lowered, depending on         whether it is cooling or heating season. An advantage of using         this thermal model is that it predicts thermal rebound if         buildings that have had their thermostat load set back return to         normal operation.     -   thermostatic space conditioning with cycling of the heating,         ventilating and air-conditioning (HVAC) unit—uses the same         first-order thermal model and simulation as for temperature         setback, but events cause a reduction in modeled power of the         space conditioning equipment to represent the cycling of HVAC         equipment.     -   stationary battery storage systems—the TIS is an input to a         simulation model that attempts to maximize the cost of energy         discharged into the grid and minimize the cost of energy used to         charge the batteries. The exchange of energy is scaled by and         limited by the modeled useable energy capacity of the batteries         and by the capacity of the bidirectional power converter that         charges and discharges power into and out from the batteries.         The responsiveness of the system may also be modified depending         on how frequently the system's owner will permit it to become         alternately charged and discharged.     -   controlled distribution voltage systems—estimate the change in         load that will accompany a change in distribution voltage during         an event. In one simplified implementation, the asset model uses         a static factor to represent the change in load as a function of         change in a feeder's voltage.     -   distributed generators—models a change in generation during         events. In most cases, the generator becomes activated during         events, and the generator supplies its nameplate rated power or         another prescribed power level during the event.     -   in-home display and portal notifications—in one implementation,         event periods are presumed to be indicated to in-home displays         or portals as a small number of discrete states (e.g., a         high-price event). The change in load is, of course, dependent         upon the election of a population of energy customers to         voluntarily turn their devices on or off. A typical change in         power is forecasted by time of day that may be scaled by the         numbers of in-home displays or portals in the population.     -   a suite of smart appliances, including washer, dryer, and         dishwasher—These appliances are similar to in-home displays in         that they notify customers of events during which the smart         appliance owners may elect to defer electrical load. In another         exemplary implementation, these appliances have additional         features by which customers may better automate decisions to         delay the appliance loads, and some energy reduction is also         achieved automatically when the appliances are in their         conservation mode. The change in load is modeled simply as a         fraction of typical appliance load by time of day and by         appliance type.

Table 5 summarizes the potential pairings of the listed exemplary asset models with appropriate event types. Examples for some of these pairings are described in the appendices below. Implementations for other pairings can be developed by those skilled in the art in view of this disclosure.

TABLE 5 Pairing of Response Characteristics with Asset Models Asset models Event-Driven Daily Events Real-Time Water heaters Y Y Y Thermostat setback Y Y Y HVAC cycling Y Y Y Battery storage Y Y Y Distribution voltage control Y Y Y Distributed generators Y Y Y In-home displays/portals Y Y Y Other smart appliances Y Y Y

3.9 Additional Observations

In a fully deployed TCS, regional transmission and generation owners formulate TIS signals by stating the temporal and locational value of resources at many transmission and generation sites in the region, and the TFS, a feedback signal, influences their resource dispatch decisions at these distributed locations.

Further, as household devices become more intelligent, there will eventually exist vast populations of flexible, responsive assets that would be active in a TCS. These assets will be available to modify their consumption at each update interval. A TCS invites the demand side to participate in the system objectives on equal footing with supply.

3.10 Interoperability

Implementations of the disclosed technology can be standardized, if desired. Standardization efforts may be at a variety of different levels. For instance, the TCS can be defined at the organization and informational level. In this regard, FIG. 15 is a diagram 1500 showing an example skeleton model of a standard transactive node and the signals that it communicates with other transactive nodes and with modules and systems, some of which can be outside the boundary of a standardized system. Typically, neighboring transactive nodes will have to agree between themselves concerning an Interoperability Framework, including the remaining interoperability levels (“Technical/Syntactical” levels). Between unrelated sites, this negotiation is unique. However, if neighbor nodes share the same owner, a common technology may be applied to all the owner's transactive nodes. The TCS standard should desirably be agnostic of the technologies by which it may be implemented.

Certain implementers can choose to define additional implementation details beyond those in the standard. The implementations might, for example, further specify the syntactical levels of interoperability. These implementations should abide by and make reference to the main standard. However, the new implementations may themselves become standards, or they may be recognized as reference implementations of TCS.

Further, implementers may desire to keep their particular code (e.g., code for a toolkit function) confidential. Such a scenario is feasible so long as the resulting signals are conformant.

Embodiments of the disclosed technology can be integrated with academic distributed control approaches. For instance, the specification of transactive signals can be harmonized with signal characteristics specified in simulation studies. An outcome of such harmonization will be that the transactive signal that represents power flow will be a complex representation. (This use of complex here is mathematical. A complex number has real and imaginary components. The real component represents real power; the imaginary component represents the flow of reactive electrical power.)

Embodiments of the disclosed technology can be harmonized with LMP approaches. For instance, the practices of LMP and TCS can be harmonized, potentially allowing the TCS approach to compete with, supplement, or gain equal footing with LMP practices.

Embodiments of the disclosed technology can also be harmonized with other TCS approaches. For example, the price-like signal used in embodiments of the TCS approach may be modeled after cost, price, or competitive bids.

4 Overall Design for Embodiments of the Disclosed Transactive Control and Coordination System

In this section, additional details concerning the overall design for embodiments of a transactive control and coordination system according to the disclosed technology will be introduced. The discussion below also provides a supplemental discussion of the transactive control signals themselves. This discussion may, in some instances, be repetitive to the discussion above but is included herein for the sake of completeness.

4.1 Architecture of an Installed System Design

The architecture of an installed system is more diverse than for typical computer network designs. For instance, an installed system comprises generation, responsive assets, the electricity transmission and distribution systems, and digital communication and intelligence. The system therefore should consider:

-   -   Physical, geographical location     -   Electrical connectivity     -   Information flow.         These components are interdependent, and a close correlation         will typically exist and be maintained between them.

4.1.1 Physical, Geographical Architecture

The physical, geographical system architecture captures the physical locations of each piece of the installed system. Physical location can be influential to transactive control because local attributes (e.g., weather) affect the behaviors of equipment, end users, and responsive assets. One tenet of transactive control is that the value of supplied electrical energy is location-dependent. Physical, geographical architecture is easily captured on a conventional map.

4.1.2 Electrical Connectivity Architecture—the Nodal Hierarchy

The electrical connectivity system architecture captures the flow of electrical energy through the installed system. One tenet of transactive control is that the communication of value and operational opportunities (e.g., the transactive signals) in a transactive control and coordination system should logically follow the pathways of electrical energy flow. Existing and future power capacity constraints are highly path-dependent.

In certain embodiments, the electrical connectivity within an installed transactive control and coordination system forms a hierarchy of nodes. Here, the word hierarchy refers to a flow direction of electrical power and is not necessarily a static assignment. Electrical transmission systems are typically mesh (not radial) systems, meaning that parallel paths in the transmission system compete to supply load. The direction of electrical power in the transmission system may change. Some of this complexity will not be discussed in detail herein because embodiments of the disclosed technology can be adapted for such complexities using software tools that properly model meshed transmission power flow.

4.1.3 Information Flow in the Transactive Control and Coordination System

The information flow design captures the flow of data and information within an installed system. An information flow architecture also indicates where manual and automated decisions are made. The information flow architecture can include, for example,

-   -   The communication channels used to transport transactive control         signals     -   The communication channels used to transport asset control         signals     -   The communication channels used for other data that supports         local, regional, and client-run experiments     -   Meter data channels through which meter data flows     -   Locations within the information flow where functional         calculations, like the estimation of future electrical load,         take place     -   Any other communication channels necessary to employ the         installed transactive control and coordination system.

The information flow architecture can also capture details about the communication channels and signals, including communication media, protocols, bandwidth, formats, software tools, exemplary functional computations, and security attributes and practices.

4.2 A Generalized Transactive Control and Coordination System

This section introduces embodiments of hierarchical transactive control that can be used in an installed system. Prior to recent efforts to build a smarter grid, most all opportunities to manage and control electrical power have been managed quite centrally from the supply side—bulk electrical generation and transmission. The role of the power grid has been simply to satisfy electrical demand—the energy consumption patterns of all the end users. Embodiments of the smart grid according to the disclosed technology will engage end users and responsive assets throughout the grid, resulting in a cooperative, more distributed approach. Transactive control can facilitate this migration to a smarter grid.

4.3 Review of Transactive Control

Transactive control is a bidirectionally negotiated system behavior. Market-like principles facilitate the negotiation; however, the signals need not be used to account for any monetary or revenue exchanges. In theory, the “winning” behaviors are optimal in some sense, having competed successfully in a “market” against alternative actions that could have been taken.

One or more of the following are characteristics that can be exhibited in embodiments of a transactive control and coordination system according to the disclosed technology:

-   -   Bidirectional communication—transactive control differs from the         similar practice of real-time nodal pricing in that it uses         dynamic feedback from its end uses.     -   Incentives and feedback are communicated via one nodal signal—at         a node, a single incentive time series is transported         downstream, and a single feedback time series is transported         upstream. Components of the incentive and feedback signals are         additively combined into one incentive and one feedback time         series. Using a single signal facilitates interoperability         between multiple operational objectives and multiple responsive         asset systems.     -   Multiple operational objectives and responsive assets are         simultaneously engaged—unlike present programmatic approaches         that create unique engineered couplings between one operational         objective and one or more responsive assets. Because operational         objectives can be integrated into a single incentive time         series, transactive control enables each responsive asset to         respond to the integrated set of operational objectives. As a         corollary, each operational objective may be acted upon by many         responsive assets.     -   The signal in a transactive control and coordination system can         be dynamic on multiple time scales—transactive control signals         are dynamic. In principle, the time intervals may be made         infinitesimally small. A transactive control and coordination         system could respond to a need for fast grid regulation, for         example, if its time intervals were made short compared to the         dynamic performance of fast regulation services. Regardless, the         responsive assets may respond according to each asset's own         dynamic capabilities and limitations. Not all parts of the         system need to agree on and use the same interval if dissimilar         interval signals can be added or interpolated to create valid         comparisons between signals that have dissimilar intervals.     -   Interoperability is facilitated—transactive control facilitates         interoperability at the organizational and informational levels,         and it allows technical layers of interoperability to become         satisfied by any, or many, appropriate standards. This attribute         helps make transactive control a worthy candidate for         interoperable, regional smart grid communications.     -   Responds 24/7—transactive control can be always active. Small         improvements and responses can be made throughout a day, not         only during the worst several hours of the year.     -   End-user friendly—by taking advantage of numerous short         intervals and distributed digital intelligence, impacts on end         users can be reduced, if not entirely eliminated. For instance,         end users should have a final say concerning their comfort and         should be provided options to temporarily opt out of responses.     -   Facilitates distributed control—transactive control facilitates         distributed intelligence and control. Centralized control is         reduced or eliminated.     -   Uses low bandwidth—the elimination of unique signals and the         distribution of control should reduce communication bandwidth.

The transactive control technique of this disclosure can be compared to other approaches to transactive control, specifically the GridWise® Olympic Peninsula Project. Table 6 summarizes the major differences between the transactive control approach used during the Olympic Peninsula Project and embodiments of the disclosed transactive control approach.

TABLE 6 Comparison between the GridWise Olympic Peninsula Project and Embodiments of the Disclosed Technology GridWise Olympic Peninsula Embodiments of the Disclosed Project Technology Electricity Combinations of fixed and Various approaches, as will be customer various dynamic price accounts. determined individually by incentives The project maintained a shadow participating utilities. Incentive market and customer accounts practices should be sustainable. that were separate from utility billing. Feedback A bid was received from every Each transactive node reports signal responsive asset every five feedback that consists of a time series minutes ($/MWhr). of expected energy consumption during each time interval into the future (kWhr/interval). Operational One single transmission line Multiple constraints, regional objectives constraint was addressed. renewable energy availability, addressed economic dispatch of resources, hydrogeneration, peak load mitigation, balancing resources, spot-market purchase mitigation, . . . Future time Not more than five minutes. To be determined (probably from one horizon to two days). Approach for Explicit clearing of the two-way Uses iterative resolution of the resolution of “market” conducted every five “market” future intervals over time. the “market” minutes. Shortest time Five minutes for real-time price To be determined (perhaps five intervals customers. minutes). supported Architecture Centralized. Information flow Enforces a nodal hierarchy, including was managed from a central plans for standardization and operations center and included extensibility of the hierarchy. the aggregator's communication Launched at multiple initial transactive servers. node sites.

Exemplary components of embodiments of the transactive control and coordination system include one or more of:

-   -   Transactive nodes—a physical point within an electrical         connectivity map of the system. Electrical energy flows through         a transactive node. A transactive node is not to be confused         with locations within the information flow map that might also         be called “nodes.”     -   Transactive signals—each node location receives an incentive         signal from upstream and generates a corresponding feedback         signal to be sent back upstream. These two signals—the         transactive incentive signal and its feedback—together are the         transactive signals.     -   Responsive assets—the “prime movers” of the transactive control         and coordination system that can modify consumption of         electrical energy (e.g., in response to the current values of         the transactive signals).     -   Enabling assets—assets like communication infrastructure and         metering that cannot by themselves modify energy consumption.         Cost-benefit analysis typically cannot be properly assessed for         an enabling asset alone because it represents only costs but no         measureable smart grid benefits. The expenses of enabling assets         are desirably allocated among and borne by truly responsive         assets.

Responsive and enabling assets are more thoroughly discussed below.

4.4 Transactive Signals

This section describes example transactive signals and their use by the demonstration.

4.4.1 Introduction to Transactive Signals

In certain embodiments of the disclosed technology, there are two transactive signals at each transactive node:

-   -   A transactive incentive signal (TIS) time series comprising the         aggregated present and future values of the electricity supplied         to and through each transactive node; and     -   The transactive load feedback signal (TFS) comprising the sum of         an estimate of the future quantity unresponsive and responsive         electrical load to be consumed by the entire load downstream         from the transactive node.

Each of the two signals is a time series, meaning that each is a vector of numbers, one for the present time interval and others for each future time interval (e.g., at least a day into the future). The time interval and horizon into the future can vary from embodiment to embodiment. In some embodiments, the time interval is five minutes. Shorter intervals than this would permit the demonstration system to provide additional ancillary services. Further, in some embodiments, shorter intervals are used for the near term and longer intervals into the signals' future. The signals' time horizon desirably extends at least to the future time when resource dispatch decisions are being made for the region.

The transactive signals at time t₀ can have the forms:

-   -   TIS={TIS(t₀), TIS(t₀+Δt), TIS(t₀+2Δt), TIS(t₀+3Δt), . . . ,         TIS(t_(f)−Δt)}     -   TFS={TFS(t₀), TFS(t₀+Δt), TFS(t₀+2Δt), TFS(t₀+3Δt), . . . ,         TFS(t_(f)−Δt)},         where TIS and TFS are the transactive incentive and feedback         signals, respectively, Δt is the selected time interval, and         t_(f) is the end of the prediction time horizon. The given time         signal series can be updated next at time t₀+Δt.

The time-series elements of these two transactive signals are paired for each future time interval. This pairing between transactive incentive signal and transactive feedback signal is illustrated in block diagram 1600 of FIG. 16, which also portrays how an upward trend in the transactive incentive signal for any future time interval should result in a corresponding downward trend in load supplied through the transactive node for that time interval. If the transactive node supplies any responsive electrical load (e.g., responsive assets that are responsive to the transactive incentive signal), the responsive electrical load should respond to changes in the transactive incentive signal. FIG. 16 indicates further that the granularity of the intervals for these signals could be relatively fine in the near term and courser into the distant future.

During the application of transactive signals, sensibility checks and default behaviors are desirably planned. For example, the nodes can be provided some independence to recognize and discount nonsensical signals that are believed to be erroneous. When no signals are received by transactive nodes, as may be the case when there has been a problem or equipment failure somewhere in the system, the nodes should again have the independence to revert to safe, bounded behaviors.

4.4.2 Transactive Incentive Signal

In particular embodiments of the disclosed technology, the transactive incentive time series is the main transactive signal. Each transactive node will typically have a unique blend of energy suppliers, upstream transmission pathways and distances, operational practices, local infrastructure, and/or downstream customers. Therefore, the values of the transactive incentive signal can be unique at a transactive node in the system.

In certain implementations, the basis for the transactive signal series at any node is a weighted sum of the transactive incentive signals received by that transactive node from immediately upstream transactive nodes that supply it electrical energy. The default approach, for example, can be to weigh the transactive signals according to the relative fraction of the node's power that is supplied from each upstream source as described below.

Each transactive node can also modify the transactive incentive signal that it relays downstream. At each transactive node, local conditions are analyzed and the incentive signal modified (or left unchanged) based on the local conditions. Modification of the incentive signal is for the purpose of influencing the behavior of responsive assets downstream from the node. The basic action at any node can be simply represented as:

TIS_(output)(t)=Weighted average(TIS_(input)(t))+New incentives(t)

TIS_(output)(t)=Weighted average(TIS_(input)(t))+New incentives(t).

Examples of how and why a transactive node will modify its transactive incentive signal include:

-   -   The expense of energy supplied at the node—those transactive         nodes that host generation have the opportunity and         responsibility to insert the initial incentive signal values for         that resource. For example, the incentive signal may reflect         fuel expenses, infrastructure expenses, and/or all other         expenses that are incurred to operate the resource. Ideally, the         sum of incentives inserted for a generator over a year or longer         should approach the sum of its true operational expenses.     -   Infrastructure constraints or congestion avoidance imposed by         the node—if the node itself becomes electrically constrained, it         should modify the transactive incentive signal to incentivize         downstream behavior that will alleviate the constraint. For         example, the modification might be set equivalent to the         incremental expense that would be incurred from the consequent         shortening of a piece of equipment's lifetime, plus the         likelihood that expenses will be incurred from outages after         exceeding equipment ratings.     -   Amortization and other expenses of installed equipment—even idle         equipment can be argued to incur expenses. One should insert an         expense for maintaining necessary infrastructure of the node.         This incentive component, for example, is part of a natural         disincentive for consuming energy far from where it is generated         and thus using transmission infrastructure.     -   Energy losses—modifications of the transactive incentive signal         may account for line, transformation, and equipment energy         losses.     -   Operational objectives that occur at business entity         boundaries—especially at business entity boundaries, the system         shall encounter new operational objectives and values that         should be respected. For example, certain utilities manage spot         market purchases that are not influential in the regional         hierarchy but become important at the boundaries of that         utility.

The formulation of the transactive incentive signal can, but need not directly, incorporate actual allocations and financial metrics used by utilities and other business entities; the transactive incentive signal can instead be formulated to allocate expenses in a way that will induce useful responses for the entity that owns a transactive node. However, a faithful transactive incentive signal formulation should approach the same overall value as for actual expense reporting over long periods of time. There is nothing that would prevent the transactive control and coordination system from supporting markets and revenue accounting in other formulations.

The incentive signal can have a variety of forms or units, but in some embodiments uses units of $/MWhr (or other equivalent, such as a number or value that is proportional (linearly or otherwise) to this unit). Thus, the signal need not be an actual price, but can be representative of a price or economic unit. One tenet of embodiments of the disclosed transactive control scheme is that items that are valued at a location in the system should be combined into one shared signal, and that can be achieved only after there is consensus about a common metric unit to be used by the signal. This principle will help enforce that business entities' operational objectives should fairly compete.

4.4.3 Transactive Load Feedback Signal

Corresponding to a transactive incentive signal time interval is a transactive load feedback signal (e.g., in the kW or other equivalent or representative unit). This transactive feedback signal time series includes the present and future electrical load that is predicted to be supplied through the transactive node during each time interval. In some embodiments, the signal is the sum of the unresponsive electric load that is not affected by the transactive signal and the responsive electric load that can monitor and respond to the transactive incentive signal.

TFS_(output)(t)=ΣTFS_(unresponsive,input)(t)+ΣTFS_(responsive,input)(t,TIS_(output)(t))

The transactive feedback signal is not a “load forecast” of the type that some utilities prepare as they plan resource commitments. There are no direct penalties to be incurred by subprojects when their transactive feedback signals prove inaccurate. The transactive control approach might diminish the importance of load forecasts in the future if the flexibility provided by transactive control can be shown to displace some of the need for predictive accuracy. Interestingly, the accuracy of a node's transactive feedback signal prediction may always be tested against the true consumption that is measured eventually at the transactive node. In some embodiments, the intelligence at a transactive node can “learn” over time to improve its own predictions. Neighboring transactive nodes learn also from an adjacent transactive node's inaccuracies and may choose to alter or suspect that transactive node's outputs.

In some embodiments, the inputs to the transactive feedback signal at a transactive node include any one or more of the following types of inputs:

-   -   Transactive feedback signals generated from transactive nodes         that are immediately downstream;     -   Transactive feedback signals generated from smart responsive         assets that are controlled from the present transactive node's         position in the hierarchy;     -   Raw unresponsive load measurements that may be subjected to         further computation or modeling to predict the remaining future         time intervals; and/or     -   Raw responsive load measurements from responsive assets that do         not themselves predict and provide transactive feedback signals         but instead rely on the transactive node to perform predictions.

4.4.4 Implications for Customer and Utility Incentives

As has been stated, the transactive incentive signal is not intended to account for monetary exchanges or revenue between regional entities. However, the transactive incentive signal could become the foundation for regional exchanges or revenues. The transactive incentive signal may also be used as a basis for customer incentives if the subprojects can establish workable shadow accounts for these customers.

4.5 Transactive Nodes

Any of the physical locations in the electrical connectivity architecture of a power system can be transactive nodes. A node is a location or piece of equipment that electrical power flows through. The term “hierarchy” is used to describe a set of transactive nodes that may extend all the way upstream to bulk generators and all the way downstream to electrical loads.

4.5.1 Responsibilities of a Transactive Node

In certain embodiments, a location or piece of equipment in the electrical connectivity architecture is described as a transactive node if it performs one or both of the following:

-   -   Accepts at least one transactive incentive signal time series         from upstream and sends a transactive incentive signal time         series downstream. If multiple transactive incentive signals are         received from upstream, a transactive node blends these         incentives into a single transactive incentive signal to be sent         downstream.     -   Accepts at least one transactive feedback time series from         downstream and sends at least one transactive feedback time         series upstream. A transactive node can further predict         electrical load and can thereby convert raw electrical load         meter readings, as necessary, into transactive feedback time         series.

A transactive node can also: modify the output transactive incentive signal to address any local operational objective that exists at the transactive node; and/or predict the responsive electric load from any responsive assets that are being controlled from the location of the transactive node.

These responsibilities of a transactive node are summarized by block diagram 1700 of FIG. 17, where the “prediction and control machine” is the intelligence (typically implemented as software executed by computing hardware associated with a transactive node) that modifies the output transactive incentive signal, predicts the behaviors of downstream electrical load, and controls responsive assets at the transactive node.

Any one or more of the following functional behaviors can be carried out by transactive nodes:

-   -   Basic transactive node functions     -   Management of electrical constraints     -   Management of electrical supply     -   Management of responsive assets.

These general functional behaviors help form the basis for a basic building-block model of a transactive node, whose models may be linked together to model the behaviors of the transactive nodes in a completed nodal hierarchy. Each of these functional behaviors is discussed in more detail below.

4.5.2 Basic Transactive Node

This section addresses the most basic functions that a point in the electrical connectivity architecture (hierarchy) performs as part of its role as a transactive node. First, a transactive node desirably is able to receive at least one transactive signal and “blend” the signals into a single transactive signal output to be sent downstream through the hierarchy. For purposes of this discussion, this basic function is termed the incentive blending function and is illustrated in block diagram 1800 in FIG. 18. Secondly, a transactive node desirably is able to receive or meter the downstream electric load that it supplies and aggregate this information and these measurements into a complete transactive feedback signal to be sent upstream through the hierarchy. For purposes of this discussion, this basic function is termed load aggregation, and is also illustrated in FIG. 18.

As a starting point for the design, the default incentive blending function can be assigned as a weighted average of the transactive incentive signals that are received at the transactive node from upstream, where the weighting is performed according to the energy received from each source during the interval. For instance, this weighted average can be formulated as:

${{TSF}_{output}(t)} = {\frac{{{{TFS}_{{input}\; 1}(t)} \cdot {{TIS}_{{input}\; 1}(t)}} + {{{TFS}_{{input}\; 2}(t)} \cdot {TIS}_{{input}\; 2}} + \ldots}{{{TFS}_{{input}\; 1}(t)} + {{TFS}_{{input}\; 2}(t)} + \ldots}.}$

It is noteworthy that the relative electrical energy to be received from multiple source inputs to a transactive node during a time interval cannot be directly controlled by the transactive node and may only be predicted imperfectly from the transactive node's limited view of the system. This might not be problematic (or even evident) for transactive nodes that exist within largely radial distribution systems, but may become more evident for transactive nodes within highly redundant transmission pathways and near dispatchable generators. This observation results from the more distributed nature of the disclosed transactive control and coordination system and can be contrasted with systems where transmission system conditions are predicted by load flow calculation methods that assume nearly complete system visibility and use simultaneous solution of the entire system's status.

The load aggregation function is conceptually simple, but complexities potentially arise from the breadth of downstream electrical load types and conditions. In principle, the purpose of the load aggregation function is simply to receive or measure electrical load that is supplied through the transactive node and to convert these measurements and this information into the transactive feedback signal, including a prediction of the entire electrical load to be supplied through the transactive node for each time interval. The transactive node can implement this functionality according to one or more of the following cases:

Case 1. If there are transactive nodes immediately downstream from the given transactive node, then the transactive feedback signals that are received from them is already in the right format and should simply be added. Case 2. The electric load that is not from responsive assets and is not supplied by another downstream node is predicted and converted into the format of the transactive feedback signal. This prediction might rely on an active model of the behaviors of the supplied load or its components. These unresponsive asset behaviors might be influenced by weather, day of week, customer habits, and/or many other conditions, but they are not affected by the transactive incentive signal. Case 3. A third case is similar to case 2 above but further includes responsiveness to the transactive node's transactive incentive output signal.

4.5.3 Constraint Transactive Node

A transactive node that manages an electrically constrained piece of equipment at the transactive node additionally may modify its output transactive incentive signal to manage this constraint. This additional function is shown in diagram 1900 of FIG. 19 in line with the downstream output of the transactive node's transactive incentive signal. This function draws upon predicted electrical load and other local information, including the knowledge of the electrical constraint magnitude.

In summary, the transactive incentive signal can be made responsive to the constraint, and the downstream responsive assets can be made to reduce or curtail their consumption when the transactive incentive signal becomes high.

In contrast to a transactive approach where price is determined by a two-way clearing of a market, embodiments of the disclosed technology base the magnitude of the transactive incentive signal on actual risks and expenses. The transactive incentive signal is therefore not a marginal price but is instead a transparent accumulation of incurred expenses. This approach responds to the criticism received by marginal pricing that it results in more, not less, expense to customers.

If a constraint is to be addressed, the transactive node can be associated with the constrained piece of equipment. This practice can help in situations where it is desirable to have only one output transactive incentive signal be necessary from the perspective of the transactive node.

In some instances, local situation information can also be received from this function, which may generate useful alerts, for example, for system operators. That is, the prediction of constrained operation at a transactive node is reflected in both the transactive incentive and feedback signals at that node, and useful notifications may be generated if thresholds are exceeded in these signals.

4.5.4 Load Transactive Node

This transactive node function addresses a node associated with a load asset and builds on the structure of a basic node. In diagram 2000 of FIG. 20, a function is shown to reside on the path of the output transactive feedback signal. This function allows local situational information to affect prediction of future electric load, but it also includes the effect of the transactive incentive signal toward predicting energy consumption by responsive load. The responsive load is the load consumption of those responsive assets that are controlled at the transactive node. (Responsive assets that are controlled at downstream nodes are also responsive, but their behaviors are already captured in the basic transactive node's summing of signals from downstream transactive nodes.)

Smaller distributed generation can be addressed by using the load transactive node functions. Distributed generators can make their decisions to run or not based on the transactive incentive signal which is provided by the load transactive node functions. When the small generator operates, it effectively reduces downstream electrical load.

The transactive node further uses its version of the transactive incentive signal to functionally control its responsive assets via a toolkit load function selected from a library of such available functions. The output of this function to the responsive assets can depend upon the control method the utility has established for that responsive asset:

-   -   Direct demand response—an event-type of response is initiated by         the responsive asset system when the transactive incentive         signal exceeds a rather extreme threshold. Events occur         infrequently.     -   Time-of-use—an event is initiated by the responsive asset system         while the transactive incentive signal is within defined         boundaries that are exceeded most days. Often used to address         system peak load. Includes peak responses where more extreme         events are recognized.     -   Real-time—a continuum of responses is provided by the responsive         asset to the transactive incentive signal. This use case is         active most, if not all, days and hours.

These responses are shown conceptually in graph 2100 of FIG. 21. Relative variations in the transactive incentive signal are shown to result in direct demand response, time-of-use (TOU), and real-time response options.

4.5.5 Supply Transactive Node

A supply transactive node function is shown in diagram 2200 of FIG. 22 and is similar to a load transactive node function. Both function types attempt to mitigate an imbalance between electrical supply and load, so it is reasonable that their forms would be similar.

This transactive node function is targeted mostly to bulk generation nodes. At these transactive nodes, the base foundation for transactive incentive signals is established. At a supply node, there may be no upstream nodes from which input transactive incentive signals could be received. The function in the path of the output transactive incentive signal is then the initial formulation of the base transactive incentive signal.

Local situational information can be generated or received by this transactive node. The supply transactive node can apply supply control (or a recommendation) if such supply generation is provided at this transactive node. Local information can also be used to inform what fuel expense and other operational expenses should be included into the initial transactive incentive signal at this location.

The incentive signal and the actual expenses of the supply desirably agree over long periods of time, but the function can (while adhering to this stated guideline) address the value of electrical generation in a way that instills useful responses by the region's responsive assets. For example, when this supply transactive node function is applied at wind farms, the created transactive incentive signal can induce the region's responsive assets to consume more of its energy while and near where the wind energy is produced.

4.6 Understanding Generalized Transactive Nodes as Combinations of the Functional Component Types

A set of transactive node functions has been introduced. These functions can be generalized as shown in diagram 2300 of FIG. 23. In particular, diagram 2300 illustrates a single model of a transactive node and its functions. Any one or more aspects of this model can be replicated throughout a transactive control and coordination system to represent a variety of types and instantiations of the system's transactive nodes.

In particular implementations of the transactive system, the output transactive incentive signal becomes an input transactive incentive signal to a transactive node that is immediately downstream; the output transactive feedback signal from a transactive node becomes the input for a transactive node immediately upstream.

4.7 Hierarchy

Block diagram 200 in FIG. 2 shows examples of significant transactive node locations that exist within a typical electric power grid. Embodiments of the transactive control technique are unique in that it addresses the power system from bulk generation to end use and back again. Ideally, and in certain embodiments, a complete hierarchy of transactive nodes is defined throughout the power system. In reality, there are parts of the electrical connectivity pathways without transactive nodes. In such cases, some nodes will perform more prediction and do so for more of a distribution system than they would do in a complete hierarchy. Further, in some cases, local constraints and other local operation objectives that might be mitigated by transactive nodes will remain unobserved.

5 Generalized Methods and Systems for Implementing Aspects of the Disclosed Technology

Having introduced the disclosed technology in the sections, this section presents general methods and systems for performing aspects of the disclosed transactive control approach. The embodiments below should not be construed as limiting and can be performed alone or in combination with any other feature or aspect disclosed herein.

FIG. 24 is a flowchart 2400 showing a generalized method for operating a transactive node in a transactive control electrical-energy-allocation system as can be used in any of the disclosed embodiments. The method can be performed using computing hardware (e.g., a computer processor or a specialized integrated circuit). For instance, the method can be performed by computing hardware associated with a transactive node where electrical energy is distributed, generated, and/or consumed.

At 2410, incentive signal data is computed. The incentive signal data can comprise data indicative of a cost of electric energy at the transactive node at a current time interval and data indicative of a forecasted cost of electric energy at the transactive node at one or more future time intervals. In certain embodiments, the current time interval refers to the imminent (or next-to-occur) interval in which the transactive node will operate.

At 2412, feedback signal data is computed. The feedback signal data can comprise data indicative of an electric load at the transactive node at the current time interval and data indicative of a forecasted load for electric energy at the transactive node at the one or more future time intervals. In certain embodiments, the current time interval refers to the imminent (or next-to-occur) interval in which the transactive node will operate

At 2414, the incentive signal data and the feedback signal data is transmitted. For example, the incentive signal data and feedback signal can be transmitted separately or together from one transactive node to each of its neighboring transactive nodes.

In certain embodiments, the data indicative of the cost of electric energy comprises data indicative of a cost of real electrical energy, reactive electrical energy, or a combination of both real and reactive electrical energies at the transactive node at the current time interval. Further, the data indicative of the forecasted cost of electric energy can comprise data indicative of a forecasted cost of real electrical energy, reactive electrical energy, or a combination of both real and reactive electrical energies at the transactive node at the one or more future time intervals. In some embodiments, the data indicative of the electric load comprises data indicative of a real electrical load, reactive electrical load, or a combination of both real and reactive electrical loads at the transactive node at the current time interval. Further, the data indicative of the forecasted load for electric energy can comprise data indicative of a forecasted load of real electrical load, reactive electrical load, or a combination of both real and reactive electrical loads at the transactive node at the one or more future time intervals.

In some embodiments, the incentive signal data further comprises data indicating a confidence level that the data indicative of the cost of electric energy at the transactive node at the current time interval is reliable (e.g., a confidence level for each time interval), and data indicating a confidence level that the data indicative of the forecasted cost of electric energy at the transactive node at the one or more future time intervals is accurate (e.g., a confidence level for each time interval). Further, in certain embodiments, the feedback signal data further comprises data indicating a confidence level that the data indicative of the electric load at the transactive node at the current time interval is accurate, and data indicating a confidence level that the data indicative of the forecasted load for electric energy at the transactive node at the one or more future time intervals is accurate.

In certain embodiments, the method further comprises receiving incentive signal data and feedback signal data from one or more neighboring transactive nodes. In such embodiments, the computation of the incentive signal data can be based at least in part on the received incentive signal data, and/or the computation of the feedback signal data can be based at least in part on the received feedback signal data.

FIG. 25 is a flowchart 2500 showing another generalized method for operating a transactive node in a transactive control electrical-energy-allocation system as can be used in any of the disclosed embodiments. The method can be performed using computing hardware (e.g., a computer processor or a specialized integrated circuit). For instance, the method can be performed by computing hardware associated with a transactive node where electrical energy is distributed, generated, and/or consumed.

At 2510, incentive signal data is received at the transactive node from two or more neighboring transactive nodes. The incentive signal data from the two or more neighboring transactive nodes can comprise data indicative of at least a cost of electric energy at a current time interval. In certain embodiments, the incentive signal data comprises data indicative of the cost of electric energy at the current time interval (e.g., the delivered unit cost of the energy at that node) and data indicative of a forecasted cost of electric energy at one or more future time intervals. In certain embodiments, the current time interval refers to the imminent (or next-to-occur) interval in which the transactive node will operate

At 2512, aggregated incentive signal data is computed based at least in part on the incentive signal data from the two or more neighboring transactive nodes. In some embodiments, the aggregated incentive signal data comprises data indicative of the aggregated cost of electric energy at the current time interval and data indicative of a forecasted aggregated cost of electric energy at one or more future time intervals. Further, in some embodiments, the aggregated incentive signal data comprises a weighted sum of the incentive signal data from the two or more neighboring transactive nodes. In certain embodiments, the aggregated incentive signal data is further modified to provide an incentive or disincentive to the further transactive node based on local conditions at the transactive node. In certain embodiments, the current time interval refers to the imminent (or next-to-occur) interval in which the transactive node will operate

At 2514, the aggregated incentive signal data is transmitted to a further transactive node (e.g., a neighboring transactive node).

In some embodiments, the received incentive signal data and the transmitted aggregated incentive signal data comprise data indicative of a cost of real electrical energy, reactive electrical energy, or a combination of both real and reactive electrical energies. In certain embodiments, the received incentive signal data further includes data indicating a confidence level of the received incentive signal data (e.g., a confidence level for each time interval). And in some embodiments, the transmitted incentive signal data further includes data indicating a confidence level of the transmitted incentive signal data (e.g., a confidence level for each time interval).

In some embodiments, the method further comprises receiving feedback signal data at the transactive node from the two or more neighboring transactive nodes, the feedback signal data from the two or more neighboring transactive nodes comprising data indicative of at least an electric load for electric energy at a current time interval; computing aggregated feedback signal data based at least in part on the feedback signal data from the two or more neighboring transactive nodes; and transmitting the aggregated feedback signal data to the further transactive node. In such embodiments, the received feedback signal data can comprise data indicative of the electric load for electric energy at the current time interval and data indicative of a forecasted load of electric energy at the one or more future time intervals, and the aggregated feedback signal data can comprise data indicative of the aggregated load of electric energy at the current time interval and data indicative of a forecasted aggregated load of electric energy at one or more future time intervals.

FIG. 26 is a flowchart 2600 showing another generalized method for operating a transactive node in a transactive control electrical-energy-allocation system as can be used in any of the disclosed embodiments. The method can be performed using computing hardware (e.g., a computer processor or a specialized integrated circuit). For instance, the method can be performed by computing hardware associated with a transactive node where electrical energy is distributed, generated, and/or consumed.

At 2610, feedback signal data is received at a transactive node from two or more neighboring transactive nodes. The feedback signal data from the two or more neighboring transactive nodes can comprise data indicative of at least an electric load for electric energy at a current time interval. In certain embodiments, the received feedback signal data comprises data indicative of the electric load of electric energy at the current time interval and data indicative of a forecasted load of electric energy at one or more future time intervals. In certain embodiments, the current time interval refers to the imminent (or next-to-occur) interval in which the transactive node will operate

At 2612, aggregated feedback signal data is computed based at least in part on the feedback signal data from the two or more neighboring transactive nodes. In certain embodiments, the aggregated feedback signal data comprises data indicative of the aggregated load of electric energy at the current time interval and data indicative of a forecasted aggregated load of electric energy at the one or more future time intervals.

At 2614, the aggregated feedback signal data is transmitted to a further transactive node.

In certain embodiments, the received feedback signal data and the transmitted aggregated feedback signal data comprise data indicative of a real electrical load, reactive electrical load, or a combination of both real and reactive electrical loads. In some embodiments, the received feedback signal data further includes data indicating a confidence level of the received feedback signal data (e.g., a confidence level for each time interval). And in certain embodiments, the transmitted feedback signal data further includes data indicating a confidence level of the transmitted feedback signal data (e.g., a confidence level for each time interval).

In some embodiments, the method further comprises receiving incentive signal data at the transactive node from the two or more neighboring transactive nodes, the incentive signal data from the two or more neighboring transactive nodes comprising data indicative of at least a cost of electric energy at the current time interval; computing aggregated incentive signal data based at least in part on the incentive signal data from the two or more neighboring transactive nodes; and transmitting the aggregated incentive signal data to the further transactive node. In such embodiments, the received incentive signal data can comprise data indicative of the cost of electric energy at the current time interval and data indicative of a forecasted cost of electric energy at the one or more future time intervals, and the aggregated incentive signal data can comprise data indicative of the aggregated cost of electric energy at the current time interval and data indicative of a forecasted aggregated cost of electric energy at one or more future time intervals.

FIG. 27 is a flowchart 2700 showing another generalized method for operating a transactive node in a transactive control electrical-energy-allocation system as can be used in any of the disclosed embodiments. The method can be performed using computing hardware (e.g., a computer processor or a specialized integrated circuit). For instance, the method can be performed by computing hardware associated with a transactive node where electrical energy is distributed, generated, and/or consumed. The method can be performed for a transactive node associated with one or more electric resources, one or more electric loads, or a combination of both electric resources and loads.

At 2710, one or more functions from a library of functions are selected. The selection can be based at least in part on the type of one or more electric resources or electric loads associated with the transactive node. In certain embodiments, the selected one or more functions are adapted for the type of electrical load or electrical supply associated with the transactive node. In some embodiments, the configuring comprises causing computing hardware used to implement the transactive node to execute a software program for performing computations using the selected one or more functions. In certain embodiments, the selected one or more functions include a function that computes data representing one or more of energy, an energy cost, or an incentive for one or more electric resources associated with the transactive node. In some embodiments, the selected one or more functions include a function that computes data representing one or more of a predicted inelastic load or changes in elastic load for one or more electric loads associated with the transactive node

At 2712, the transactive node is configured to compute transactive signals using the selected one or more functions.

In some embodiments, the method can comprise accessing a database storing the library of functions (e.g., a locally stored database or a database remotely located from the transactive node).

Further, the library of functions can be an extensible library. For example, the library can be expanded to include newly formulated functions. Further, in some implementations, existing functions may be selected from the library, edited by a relevant party (e.g., a utility or system administrator), and returned to the library as a newly available function with modified features and capabilities. The parties that have access to editing and adding library functions can vary from implementation to implementation, and can encompass a wide variety of parties involved in the power transmission infrastructure. In some instances, the parties who can edit and/or add functions is limited to some selected group (e.g., system regulators or to a single utility).

Also disclosed herein are several embodiments for systems for distributing electricity. One of the disclosed systems is a system for distributing electricity, comprising: a plurality of transactive nodes, each transactive node being associated with one or more electric resources, one or more electric loads, or a combination of one or more electric resources and loads; and a network connected to the transactive nodes to facilitate communication between the transactive nodes. In these embodiments, the transactive nodes are configured to exchange incentive and feedback signals with one another in order to determine an electrical demand in the system for a current time interval and to provide an electrical supply sufficient to meet the electrical demand for the current time interval. In certain embodiments, the current time interval refers to the imminent (or next-to-occur) interval in which the transactive nodes will operate

In certain embodiments, the transactive nodes are further configured to exchange incentive and feedback signals for two or more future time intervals in addition to the incentive and feedback signals for the current time interval. In some embodiments, the two or more future time intervals have increasingly coarser granularity. In certain embodiments, at least one of the transactive nodes modifies one or both of its incentive or feedback signals in response to previously received incentive and feedback signals. In some embodiments, the at least one of the transactive nodes is associated with an elastic load, and wherein the modified incentive or feedback signals corresponds to a predicted change in the elastic load. In certain embodiments, the at least one of the transactive nodes is associated with an electrical resource, and the modified incentive or feedback signals corresponds to a change in the electrical resource. In further embodiments, the at least one of the transactive nodes is associated with an electrical resource, and the modified incentive signals correspond to a change in local conditions.

In certain embodiments, one or more of the transactive nodes compute their respective incentive and feedback signals using functions selected from a library of functions. Still further, in some embodiments, the incentive and feedback signals further include confidence level data indicating a respective reliability of the incentive and feedback signals.

Another system disclosed herein is a system for distributing electricity, comprising: a plurality of transactive nodes, each transactive node being associated with one or more electric resources, one or more electric loads, or a combination of one or more electric resources and loads; and a network connected to the transactive nodes and facilitating communication between the transactive nodes. In these embodiments, the transactive nodes are configured to exchange sets of signals with one another in order to determine an electrical demand in the system for a current time interval and to provide an electrical supply sufficient to meet the electrical demand for the current time interval. Each set of signals includes signals for determining the electric loads and supplies for the current time interval as well as signals for determining the electric loads and supplies for two or more future time intervals. In certain embodiments, the current time interval refers to the imminent (or next-to-occur) interval in which the transactive nodes will operate

In some embodiments, the future time intervals have increasingly longer durations as the time intervals are farther into the future relative to the current time interval. In other embodiments, the transactive nodes are configured to update the values of the sets of signals at an update frequency, the update frequency corresponding to a duration of the current time interval. In some embodiments, the transactive nodes are configured to exchange the set of signals with one another iteratively over time such that the signals for a respective time interval stabilize as the respective time interval approaches the current time interval.

In certain embodiments, the transactive nodes are configured to exchange the set of signals with one another on an asynchronous event-driven basis or a clock-driven basis. In some embodiments, a respective set of the transactive nodes are configured to iteratively exchange a set of signals with one another until the exchanged set of signals converges to within an acceptable degree of tolerance. In certain embodiments, a transactive node in the respective set of the transactive nodes is further configured to transmit an updated set of signals when local conditions at the transactive node cause the updated set of signals to deviate from a previously transmitted set of signals beyond a relaxation criterion. In some embodiments, the sets of signals further include confidence level data indicating a respective reliability of the exchanged signals (e.g., a confidence level for each time interval).

Another system disclosed herein is a system for distributing electricity, comprising: a plurality of transactive nodes, each transactive node being associated with one or more electric supplies, one or more electric loads, or a combination of one or more electric supplies and loads; and a network connected to the transactive nodes and facilitating communication between the transactive nodes. In these embodiments, the transactive nodes are configured to exchange sets of signals with one another in order to determine an electrical demand in the system for a current time interval and to provide an electrical supply sufficient to meet the electrical demand for the current time interval, a respective one of the transactive nodes being configured to compute its incentive and feedback signals using one or more functions selected from a library of functions. In certain embodiments, the current time interval refers to the imminent (or next-to-occur) interval in which the transactive nodes will operate

In certain embodiments, the one or more functions selected from the library of functions are selected based on the type and number of electrical supplies and electrical loads with which the respective transactive node is associated. The one or more functions can be selected from a group of resource functions comprising one or more of: (a) a resource function adapted to account for imported electrical energy, (b) a resource function adapted to account for a renewable energy resource, (c) a resource function adapted to account for fossil fuel generation, (d) a resource function adapted to account for general infrastructure cost, (e) a resource function adapted to account for system constraints, (f) a resource function adapted to account for system energy losses, (g) a resource function adapted to account for demand charges, and (h) a resource function adapted to account for market impacts. The one or more functions can also be selected from a group of load functions comprising one or more of: (a) a load function adapted to account for a bulk inelastic load, (b) a load function adapted to account for an event-driven demand response, (c) a load function adapted to account for a time-of-use demand response, and (d) a load function adapted to account for a real-time continuum demand response.

In some embodiments, the respective one of the transactive nodes controls one or more elastic loads and adjusts the one or more elastic loads in response to the feedback and incentive signals received at the respective one of the transactive nodes. In certain embodiments, the one or more functions are implemented by individual software modules that can be combined with one another to implement the desired transactor behavior for the respective one of the transactive nodes.

In certain embodiments, through the use of the one or more functions, the respective one of the transactive nodes computes a control signal selected from a set of signed whole numbers and communicates the computed control signal to one or more loads, resources, or loads and resources associated with the respective one of the transactive nodes. The computed control signal can be interpreted by an electrical generator or set of electrical generators as a fraction of the generator's or generators' rated generation capacity. The computed control signal is interpreted by an electrical load or set of electrical loads as a fraction of the load's or loads' rated power.

It should be understood that in embodiments of the disclosed technology, a transactive node may host multiple toolkit functions, including any combination of multiple resource and incentive functions, multiple load functions, or combinations of both resource and incentive and load functions. For instance, the resource and/or incentive functions used at a transactive node will typically depend on the location of the transactive node in a power grid topology, and on the one or more resources and/or loads for which the transactive node is responsible. This ability to “mix and match” resource and incentive functions while still maintaining a common transactive signal communication structure gives embodiments of the disclosed technology wide flexibility and scalability for implementing a transactive control system.

6 Further Details and Embodiments

Having introduced the disclosed technology, this section includes four supplemental Appendices that provide additional details and configurations that can be used in implementations of the technology. The specific implementations disclosed below should not be construed as limiting. Further, any one or more of the features or aspects disclosed below can be used alone or in conjunction with any other feature or aspect of the disclosed technology discussed herein. Some portions of the appendices may, in some instances, be repetitive to other portions of this application, but such portions are included for the sake of completeness.

6.1 Appendix A Transactive State Model 6.1.1 Purpose

A transactive control and coordination system is a network of loosely connected, interacting transactive nodes. This appendix states a high-level state model for a transactive node and types of connections that a transactive node desirably manages. This appendix should provide valuable guidance to system designers who are implementing a transactive control and coordination system from the perspective of a transactive node.

This appendix defines and discusses

-   -   example attributes of a transactive node and four example types         of connections     -   the organization of these attributes into groups—transactive         node, general connection, transactive neighbor, system manager,         asset, and local information     -   example allowed states within the high-level transactive node         state model     -   example functions and events by which attributes become changed         and by which the states are navigated in this state model     -   example state transition tables and diagrams for the respective         transactive node and its connections.

6.1.2 Structure

In some embodiments, a transactive node manages its own set of attributes and additionally manages additional types of connection. In certain implementations the transactive node manages four types of connections—connections to transactive neighbors, system managers, assets, and local input information. All four connection types can share a set of connection attributes in common in order to manage connections between this transactive node and each transactive neighbor, system manager, asset, or local input information. An example of this structure has been laid out in diagram 2800 in FIG. 28.

6.1.3 Transactive Node States and State Diagram

In certain embodiments, a transactive node has five states available to it as shown in the state transition diagram 2900 of FIG. 29:

-   -   1—New or Terminated—initial and terminal state where the         transactive node attributes are not defined. The transactive         node leaves and returns to this state by running or terminating         an executable program.     -   2—Under Local Control—intermediate state where the transactive         node executable process is up and running, but the transactive         node and its connections are not adequately configured. Few, if         any, of this transactive node's connections have been completed         between this transactive node and its transactive neighbors,         system managers, assets, or local information sources, which         collectively will be referred to as the transactive node's         “connection partners.” A transactive node enters this state when         a transactive node executable program is run or when a         Configuration Test fails.     -   3—Configured—intermediate state where certain transactive node         attributes (those transactive node attributes having asterisks         in FIG. 28) have been defined and each of the connections that         this transactive node manages is also in its Configured state. A         transactive node enters this state by passing a Configuration         Test or by failing a Connection Test.     -   4—Connected & Configured—a transactive node state that has been         Configured and now each of the connections that this transactive         node manages is in its Connected (or temporarily in its Lost         Connection) state. A transactive node enters this state by         passing a Connection Test, by receiving and accepting a Halt         Operations command, or by encountering a Fatal Operational         Event.     -   5—Operational—a transactive node that has been Connected &         Configured and which now interacts with its connection partners         according to its algorithmic responsibilities of membership in a         transactive control and coordination system. The algorithmic         responsibilities are addressed elsewhere as a “toolkit         framework” of computational algorithms and a suite of “toolkit         library functions” that may be incorporated to represent the         more unique and individual algorithmic responsibilities of         transactive nodes. The toolkit framework and the toolkit library         functions are described in more detail in Appendices B and C. A         transactive node enters this state by receiving and accepting an         Operate command.

The identifying numbers that have been applied to the functions and events in FIG. 29 are derived from the prior and end states. A letter is appended wherever multiple functions or events achieve the same state transition. For example, the function numbered “54b” (e.g., a Halt Operations command in FIG. 29), is the second state transition that has been defined from state “5” to state “4.” These same function and event numbers will be used in corresponding state transition table.

6.1.4 Connection States and State Diagram

Each connection has four allowed states as shown in diagram 3000 of FIG. 30. The only details that really change between the four types of connections are those attributes that are tested if a Connection Configuration Test is to be passed for a given connection. These are the connection states and their descriptions:

-   -   1—Listed—a connection has been listed when its identifier         appears among those in any of these corresponding connection         attribute lists:         -   49—List of Transactive Neighbors (a transactive node             attribute)         -   50—List of System Managers (a transactive node attribute)         -   51—List of Assets (a transactive node attribute)         -   38—List of Local Information Connections (an asset             connection attribute)     -   There is no expectation that any of the corresponding attributes         have been configured in this state. A connection reaches this         state by becoming listed in one of the attributes above, which         may occur as a transactive node executable program is being run         or thereafter using the Configure command. This is an initial         and terminal state of any connection.     -   2—Configured—certain attributes (see asterisks in FIG. 28) of         this connection have been configured and are not empty. This         connection enters this state by passing a Connection         Configuration Test, by accepting a Disconnect command that has         been directed to this connection, or when a Terminate Connection         Event occurs after this connection has been in its Lost         Connection state, which event indicates that either a timeout         duration has expired or that too many Loss of Connection Events         have occurred in the past hour or day. This is an intermediate         state.     -   3—Connected—a communication link (a “connection”) has become         successfully established between this transactive node and one         of its connection partners via this connection. A connection         enters this state by receiving and accepting a Connect command         or by having the connection re-established from a Lost         Connection state by a Connect command or a Re-Establish         Connection Event. This is an intermediate state, but a         connection should be expected to remain in this state most of         the time.     -   4—Lost Connection—the state of a connection while the connection         between this transactive node and one of its connection partners         via this connection has become broken or severed. This temporary         intermediate state may be entered by a connection only by a Loss         of Connection Event. The connection should thereafter be either         re-established by a Connect command or Re-Establish Connection         Event, or the connection should become disconnected by a         Disconnect command or by a Terminate Connection Event.

Again, the identifying numbers and letters that prepend the functions and events in FIG. 30 are derived from prior and end states and will be used also to identify these same transitions in state transition tables.

6.1.5 The Meaning of Attribute Dictionary Columns

Table 7 is a dictionary of example attributes that can be used to define the state of a transactive node. Later in this appendix, attribute dictionaries will be presented to address attributes of the four types of connections. The meanings of the columns in these dictionary tables are as follows.

-   -   Attribute—structured list of attributes (properties,         characteristics) defines the pertinent properties of a class of         objects. Assigning specific values to the full set of         attributes, creates a specific instance or member of the class.         Grouping certain attributes into subsets defines the states of         an object, including a single start state, one or more         intermediate states, and one or more final states.     -   Attribute Name—a string of alphanumeric (alphabetic, numeric)         and possibly special characters given to the attribute for         reference.     -   Description—an easy-to-read narrative about the attribute,         clearly distinguishing it from other attributes.     -   Role—the reason the attribute is important for: 1) the         definition of an object, and 2) the application of an attribute         in the process that directs actions to instantiate a specific         object.     -   Type—the attribute may represent a type of number, character         string, a pointer to a procedure, set of algorithms, names of         other classes, an address, or an array of types.     -   Format—the specific arrangement of the characters or the parts         of the assigned attribute value(s).     -   Range of Values—the specific set of values a process may assign         to an attribute, such as least value and greatest value for         numbers.     -   Security—the level of security assigned to an attribute, the         identification of the entities (people, systems) authorized to         access an attribute, and whether the entities have the right         only to read the value of the attribute or to both read and         write the attribute value(s).

6.1.6 Transactive Node Attribute Dictionary

The transactive node attribute group contains those attributes that stand alone and refer to one transactive node and its transactive node state model. An example attribute dictionary is shown in Table 7.

Table 8 that follows is a summary of which of these attributes can be added, checked, or modified by the set of commands and events that occur within the state transition table (Table 7), as were introduced in the state transition diagram 2900 of FIG. 29.

TABLE 7 Dictionary of the Transactive Node Attributes Attribute Range of No. Name Description Role Type Format values 1 Node ID Unique ID This Character 0-9, A-Z See topology of this transactive string Example: for the transactive node's name “UT-01” transactive node. that may be control and used to refer coordination to it. system This is a where attribute that transmission is desirably zone, found to balancing have been authority, configured utility, and during site names Configuration have been Tests. stated. 3 Node The type of Character TZ, BA, UT, Type transactive string ST control node. Four types have been identified: Transmission Zone (TZ) Balancing Authority (BA), Utility (UT), Site (ST) 4 Geographical The Perhaps (latitude, (−90 to 90, 0-360) Location representative useful for longitude) degrees of Node physical future global (-pi to pi, 0-2 * location of information pi) radians this system (GIS) Default transactive representations. value: node. (null, null) 5 Node The To keep Two “Filename, “Filename” Version* implementation track of alphanumeric ##.##”, should be an version successions items where ##.## allowable for the of software are the executable instantiated during major and filename. transactive incremental minor “##.##” major node at the improvements, version and minor time the troubleshooting, numbers of versions Run Node testing. this file, anticipated Executable This is an respectively. from “0.00” command is attribute that to “99.99”. issued. is desirably This found to executable have been file configured represents during a “version” Configuration for the Tests. transactive node overall. 7 Node The state of Unambiguous Single Example: “1” “1” - New or Status* this representation integer Terminated, transactive of the state “2” - Under node within of this Local this state transactive Control, “3” - model. node within Configured, this state “4” - model. Connected & This is an Configured, attribute that “5” - Operational is desirably found to have been configured during Configuration Tests. 8 Mode The current Single “Experimental”, mode of character “Production”, operation. string “Test” 9 Update The The update Single Integer From 1 to Frequency* frequency frequency integer Example: 3600. The used to may change “12” Demonstration update TIS between will most and TFS. testing and often use Units are operation. “12”, “updates This is an meaning one per hour”. attribute that update is is desirably performed found to every 5 have been minutes. configured during Configuration Tests. 16 Electrical The logical Character Varied Varied Topology location of a string Location transactive node in an electrical system 18 Time* Present Time is used See UTC See UTC time in UTC to mark standard. standard Format. when node Time is state coordinated transitions across the occur and system of also to transactive support nodes to timing of within 500 events milliseconds, related to or so. 9 - Update Frequency. Each transactive node calculates transactive signal interval start times starting from this, the present time. This is an attribute that is desirably found to have been configured during Configuration Tests. 21 Processing The time The time Varied Varied Varied Time delay for delay is used Delay this node to manage within the the time processing sequence time interval relationships for the system of transactive control nodes 22 Time Out The time to If expected Varied Varied Varied wait for TIS/TFS receipt of are not TIS and received TFS from before the adjacent time out then nodes the node proceeds with available information and reports an associated change in data quality values 34 Resource A storage See toolkit List of Expected to Reasonable Schedules location framework. series. The be very ranges may and Cost described This storage individual similar to be asserted. Buffer in the toolkit location has records will TIS and framework. data that is probably TFS. Records of relevant to resemble See the this storage the TIS and toolkit location formulation TFS. See framework. possess of both the toolkit information TIS and framework. about TFS. resources and incentives, most of which are being applied via toolkit functions. 38 Current The series A storage One series See the See toolkit IST Series of interval location of times toolkit framework. Buffer start times described in using UTC framework. The (IST) that the toolkit standard. Series of Demonstration have been framework. See toolkit times in has calculated An interim framework. UTC defined 56 and will be data storage standard intervals. used to location format. The intervals define the within the can align intervals of toolkit with one of transactive framework. the 12 major signals that Refer to the division of an are being toolkit hour. formulated. framework. 39 Input A storage Holding List of TIS See Refer to Transactive location place for and TFS transactive range Signals described most recent (e.g., a list signal attributes of Buffer in the toolkit transactive of series). formats and TIS and TFS. framework. signals that See toolkit XML Records have been framework. schema. include at received. least the Holds at most least recently attributes received 23 - Receive transactive TIS Buffer signals. and 24 - Receive TFS Buffer, but may also retain records of prior examples. An interim data storage location within the toolkit framework. Refer to the toolkit framework. 40 Resource A storage Place where List of Various. See Various for and location the input various the toolkit records that Incentive described “other local items and framework. should be Input in the toolkit conditions” series data See defined in Buffer framework. that will be as should individual toolkit invoked by be defined toolkit functions. resource and for each resource incentive toolkit and toolkit resource incentive functions and functions, should be incentive where the held and function. contents and managed. Refer to formats Attribute toolkit should be 25 - Local resource specified. Information and Source incentive states the functions sources that that are should used at supply the this contents of transactive this storage node location. where An interim these data storage specifications location should within the be made. toolkit framework. Refer to the toolkit framework. 41 Load A storage Place where List of Various. See Various for Function location the input various the toolkit records that Input described “other local items and framework. should be Buffer in the toolkit conditions” series data See defined in framework. that will be as should individual toolkit invoked by be defined toolkit load functions. load toolkit for each functions, functions toolkit load where the should be function. contents and held and Refer to formats managed. toolkit load should be Attribute functions specified. 25 - Other that are Local used at Conditions this Source transactive states the node sources that where should these supply the specifications contents of should this storage be made. location. An interim data storage location within the toolkit framework. Refer to the toolkit framework. 42 Output A storage The One TIS See TIS See range TIS Buffer location formulated attributes of described TIS is held TIS in the toolkit here and framework. may be Place replaced and where further updated updated until TIS is held it is finally until it can distributed to be transactive distributed. neighbors (and maybe other entities). See attribute 12 - Send TIS Targets. An interim data storage location within the toolkit framework. Refer to the toolkit framework. 43 Output A storage The One TFS. See TFS See range TFS location formulated attributes of Buffer described TFS is held TFS in the toolkit here and framework. may be Place replaced and where further updated updated until TIS is held it is finally until it can distributed to be transactive distributed. neighbors (and maybe other entities). See attribute 13 - Send TFS Targets. An interim data storage location within the toolkit framework. Refer to the toolkit framework. 44 Total A storage Sum of List of Modeled Represents Predicted location average series. after, or total average Resource described power that is Contents identical to, generated Buffer in the toolkit generated should be a TFS power and framework. within or similar to format. imported imported into TFS with power during a transactive same an interval. node during format. Reasonable future ranges can intervals. be stated, Compared but there is against total no such test load during in the the present formulation model. of TFS series. An interim data storage location within the toolkit framework. Refer to the toolkit framework. 45 Inelastic A storage Records are List of Modeled Records of Load location the inelastic series. after, or this list Prediction described load Contents identical to, represent the Buffer in the toolkit predicted should be a TFS load being framework. from one similar to format. modeled by toolkit load TFS with a toolkit load function for same function. future format. Reasonable intervals. ranges can Used to be stated, predict total but there is load at future no such test intervals. in the An interim present data storage model. location within the toolkit framework. Refer to the toolkit framework. 46 Elastic A storage Records are List of Modeled Records of Load location the changes series. after, or this list Prediction described to elastic Contents identical to, represent the Buffer in the toolkit load that are should be a TFS change in framework. predicted similar to format. elastic from one TFS with component toolkit load same of a load that function for format. is being future modeled by intervals. a toolkit load Used to function. predict total Reasonable load at future ranges can intervals. be stated, An interim but there is data storage no such test location in the within the present toolkit model. framework. Refer to the toolkit framework. 47 Predicted A storage An interim List of Modeled Records of Inelastic location data storage series. after, or this list and described location Contents identical to, represent Elastic in the toolkit within the should be a TFS total load of Load framework. toolkit similar to format. a transactive Buffer An interim framework. TFS with node. data Refer to the same Reasonable storage toolkit format. ranges can location framework. be stated, within the An interim but there is toolkit data storage no such test framework. location in the Refer to the within the present toolkit toolkit model. framework. framework. Refer to the toolkit framework. 49 List of List of This Comma- Example #1: See system Transactive transactive transactive separated “UT06”, topology. Neighbors nodes with node list of which is the List should which this declares character Demonstration's include transactive transactive strings identifier for nearby node neighbors an transactive exchanges that it plans demonstration nodes with electrical to interact utility. which this energy and with. A transactive will transactive node expects therefore neighbor that to exchange exchange appears on energy. transactive this list is Naming signals. eligible to practice enter its should be Listed state the same after its 52 - here and for Transactive attribute 52 - Neighbor ID Transactive has become Neighbor ID, a configured. Transactive This attribute Neighbor is checked attribute. during Configuration Tests and Connection Tests to see if expected transactive neighbors have become Configured and Connected. 50 List of List of This is the Comma- Example #1: See system System entities of a attribute by separated “EI01” to topology. Managers transactive which this list of represent Naming control and transactive character the system practice coordination node strings manager, should be system declares from which the same that will be which system here and for granted at entities it will management attribute least limited allow to command 53 - System permission make will likely be Manager ID, to make system received. a System system management Example #2: Manager management commands. “UT06”, attribute. commands The system which is the to this managers Demonstration's transactive instantiate a identifier for a node. A connection, demonstration system and this utility, manager transactive which may may be, but node be both a is not accepts a system necessarily, responsibility manager to also a to maintain the transactive the transactive neighbor. connection nodes that it to each owns and a system transactive manager. A neighbor, system too. manager in this list is eligible to enter its Listed state. For each Listed system manager, this transactive node should manage and monitor its state to enter either the 3 - Configured or 4 - Configured & Connected transactive node states, and for which Configuration Tests and Connection Tests are conducted. 51 List of List of This is the Comma- Example #1: See toolkit Assets generation attribute in separated “AV01” to framework. resources, which a list of represent an See incentives, transactive character asset respective and loads node strings system of toolkit that are declares its Avista function for a engaged assets. Each Utilities. given asset. and used asset should Naming by this be practice transactive accompanied should be node. by a toolkit the same function that here and for defines its attribute 2 - predicted Asset ID, an participation Asset in ways that attribute. affect transactive signals that are formulated at this transactive node. The assets listed here are eligible to enter their Listed states after attribute 2 - Asset ID has been configured. This attribute is checked during Configuration Tests and Connection Tests to see if expected assets have become Configured and Connected. 57 Interval An ordered This attribute Comma- Demonstration Integer Durations* list of along with separated example: values interval 58 - list of {5, 15, 60, between 1 durations in Numbers of integers 360, 1440}, and 1440. minutes Intervals that representing An allowed that will be states the represent 5 minutes, number of used by this durations of interval 15 minutes, series transactive the intervals durations 1 hour, 6 elements node as it that this in minutes hours, and 1 may be formulates transactive day. The 1- specified in its node will day intervals the future but transactive represent in are most will not be an signals. each of the distant into issue for the Order is transactive the future. Demonstration from first to signals that it In the above that will most calculates. example, the use only 5 distant into The number last sample different the future. of series of each interval elements in duration has durations. this attribute a flexible Note that this and in 58 - duration that approach Numbers of may vary that uses Intervals between the integer should be present and minutes will identical at the following limit the the times durations. practice of transactive This is done intervals that signals are to keep are shorter being intervals than 1 calculated. aligned with minute in the This attribute hourly future. creates no market data. The number expectation of series that elements in transactive this attribute neighbors and in 58 - will have Numbers of used the Intervals same should be interval identical at durations. the times This transactive transactive signals are node should being be quite calculated. flexible in its ability to receive and interpret diverse time series information. 58 Numbers An ordered This attribute Comma- Demonstration No explicit of list of the along with separated example: limit has Intervals* number of 57 - Interval list of {12, 20, 18, been placed each of the Durations integers 4, 2}, on the 57 - Interval states the that representing magnitude of Durations number of represent that there each that will be the intervals the will be 12 5- element; used by this of each number of minute, 20 however, an transactive duration that each 15-minute, element node as it this corresponding 18 1-hour, 4 would formulates transactive interval 6-hour, and unlikely be its node will duration 2 1-day greater than transactive represent in that is intervals. 10,080 - the signals. each of the listed in The last number of Order is transactive 57 - member of minutes in a from first to signals that it Interval each interval week. most calculates. Durations. duration An allowed distant into The number (e.g., the number of the future. of series 12^(th), 32^(nd), series elements in 50^(th), and elements this attribute 54^(th) may be and in 57 - intervals) specified in Interval varies in the future but Durations duration will not be an should be between the issue for the identical at durations of Demonstration the times the present that will transactive and next use only 5 signals are intervals. different being interval calculated. durations. This attribute The number creates no of series expectation elements in that this attribute transactive and in 57 - neighbors Interval will have Durations used the should be same identical at intervals. the times This transactive transactive signals are node should being be quite calculated. flexible in its ability to receive and interpret diverse time series information.

TABLE 8 Ways in Which Transactive Node Attributes may be affected by this State Model's Commands and Events Handle Run Node Halt Terminate Con- Con- Handle Fatal Non-Fatal Attribute Executable Configure Operate Operations Node figuration nection Operational Operational # Attribute Name Command Command Command Command Command Test** Test** Event Event 1 Node ID* ++ − C 5 Node Version* ++ − C 7 Node Status* (C)++ C0 C0 C0 C− C0 C0 C00 C 9 Update Frequency* + +0 − C (C) (C) 18 Time* + +0 − C (C) (C) 57 Interval Durations* + +0 − C 58 Numbers of Intervals* + +0 − C 49 List of Transactive + +0 − C C Neighbors 50 List of System Managers + +0 − C C 51 List of Assets + +0 − C C 34 Resource Schedules and + +0 − Cost Buffer 38 Current IST Series + +0 − Buffer 39 Input Transactive Signals + +0 − Buffer 40 Resource and Incentive + +0 − Input Buffer 41 Load Function Input + +0 − Buffer 42 Output TIS Buffer + +0 − 43 Output TFS Buffer + +0 − 44 Total Predicted Resource + +0 − Buffer 45 Inelastic Load Prediction + +0 − Buffer 46 Elastic Load Prediction + +0 − Buffer 47 Predicted Inelastic and + +0 − Elastic Load Buffer 4 Geographical Location of + +0 − Node 3 Node Type + +0 − 8 Mode + +0 +0 +0 − +0 +0 16 Electrical Topology + +0 − Location 21 Processing Time Delay + +0 − 22 Time Out + +0 − (C) (C) *These Node attributes will be checked and should be configured (not empty) during a Configuration Test. **The Configuration and Connection Tests will additionally check the Asset attribute 38 - List of Local Information and the statuses of connections. “C” = condition checked; “(C)” = condition possibly checked; “++” = “should establish new attribute content”; “+” = “may establish new attribute content”; “−−” = “should remove existing attribute content; “−” = “may remove existing attribute content”; “00 = “should modify existing attribute content”; “0” = “may modify existing attribute content”

6.1.7 Functions and Events of the Transactive Node State Model Run Node Executable(Filename) Command

-   -   Command Parameters         -   Filename—Filename that should be found in and run from a             known file directory. If Filename cannot be found, fail in             condition F1.     -   Command Logic         -   If Filename cannot be recognized or located, then reply             -   “Command failed—(F1) File could not be found”         -   If this transactive node is already running an executable             file, as can be determined by transactive node attribute             7—Node Status being in a valid, defined state or other             evidence that the executable is running, then reply             -   “Command failed—(F2) Node executable is already                 running.”         -   If the entity that made this command is not the local system             manager and is not found to have been granted permission to             make this command by attribute 31—Connection Partner's             System Management Permissions, then reply             -   “Command failed—(F3) Lacking permission to make this                 command”         -   If after running Filename the attributes 1—Node ID, 5—Node             Version, 7—Node Status, and 18—Time have not become             configured, then do not run the node executable. Reply             -   “Command failed—(F4) Critical transactive node                 attributes were not configured”         -   If the node executable fails to run for any other reason,             reply             -   “Command failed—(F5) Unknown reason”         -   Otherwise,             -   The node executable runs to completion and its process                 remains active, including the management present time                 18—Time in UTC format.             -   Set attribute 7—Node Status=“2” (state 2—Under Local                 Control).             -   Populate attributes 1—Node ID, 5—Node Version, and                 18—Time with the contents supplied by Filename. These                 attributes may not be empty at the successful conclusion                 of this command.             -   Additionally, any other attribute may be populated at                 the time the node executable is run.             -   Reply, “Command succeeded—(S1)”

Configure( ) (Node Attributes) Command—a flexible command that is applicable to the transactive node as well as to the other connections that a transactive node manages. An important concept in the use of this command is that the connection's identifier should be stated before any of its attributes may be modified. Because this section is addressing only the transactive node state model, the only attributes that will be addressed in this section are transactive node attributes for this transactive node.

-   -   Command Parameters         -   ConfigureFile=(Filename)—If a file is named using this             parameter, a command script will be read from Filename found             in a known file directory. It is recommended that Filename             should contain scripted parameters as would be used in line             with the command.         -   Any combination of the following comma-separated, in-line             command parameters may be used and in any order:         -   Node=(1—Node ID)—(Optional) Should match the identity of             this transactive node.             -   NodeAttribute=attribute #, attribute value 1[[,                 attribute value 2], . . . ]—This parameter may be used                 to initialize or change the contents of any Node group                 attribute except attribute 1—Node ID, 5—Node Version,                 7—Node Status, or 18—Time.     -   Command Logic         -   If the entity that made this command is not the local system             manager and if the entity has not been explicitly given             permission to make this system management command among the             commands in its 31—Connection Partner's System Management             Permissions, then reply             -   “Command failed—(F1) Permissions do not include this                 command”         -   If attribute 7—Node Status=“5” (state 5—Operational), then             reply             -   “Command failed—(F2) Configure command not allowed from                 Operational state.”         -   If Filename cannot be found, reply             -   “Command failed—(F3) File cannot be found or opened”         -   If the Node ID does not match the presently configured Node             ID, then reply             -   “Command failed—(F4) Incorrect node ID”         -   If the node attribute number does not match a known Node             attribute number (e.g., is not a member of {3, 4, 8, 9, 16,             21, 22, 34, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 49, 50             or 51}), then reply             -   “Command failed—(F5) Command did not address known node                 attributes”         -   If the command cannot be completed for any other reason,             reply             -   “Command failed—(F6) Unknown reason”         -   Otherwise,             -   Reply, “Command succeeded—(S1)”             -   Finalize any changes to transactive node attributes that                 were specified in the file or on the command line.             -   Run a Configuration Test.             -   Run a Connection Test.

Configuration Test( )—this is neither a system management command nor an event, but it is a test of the present configuration that should be conducted automatically by a transactive node after a successful Configure( ) command. It is permissible that the test may be run more often, but the outcome should not be expected to change unless a successful Configure( ) command occurs.

-   -   Parameters—None.     -   Test Logic         -   If upon checking attribute 7—Node Status, this transactive             node is found to be in state “5” (5—Operational), then             -   Test passed—(S1) The Operational state is necessarily                 Configured.             -   No further tests are required. No state transition                 occurs. No attributes are changed.         -   If any of the attributes 1—Node ID, 5—Node Version, 7—Node             Status, 9—Update Frequency, 18—Time, 57—Interval Durations,             or 58—Numbers of Intervals have not yet been configured and             are therefore empty, then             -   Test failed—(F1) The transactive node is not configured.             -   Attribute 7—Node Status=“2” (state 2—Under Local                 Control).         -   If for any transactive neighbor connection listed in 49—List             of Transactive Neighbors a corresponding 52—Transactive             Neighbor ID has not been established, then             -   Test failed—(F2) Not all transactive neighbors have been                 Listed.             -   Attribute 7—Node Status=“2” (state 2—Under Local                 Control).         -   If for any system manager connection listed in 50—List of             System Managers a corresponding 53—System Manager ID has not             been established, then             -   Test failed—(F3) Not all system managers have been                 Listed.             -   Attribute 7—Node Status=“2” (state 2—Under Local                 Control).         -   If for any asset connection listed in 51—List of Assets a             corresponding 2—Asset ID has not been established, then             -   Test failed—(F4) Not all assets have been Listed.             -   Attribute 7—Node Status=“2” (state 2—Under Local                 Control).         -   If for any Listed transactive neighbor (e.g., one for which             a 52—Transactive Neighbor ID has become established) its             32—Connection Status is either undefined or “1” (connection             state 1—Listed), then             -   Test failed—(F5) Not all transactive neighbors have                 become configured.             -   Attribute 7—Node Status=“2” (state 2—Under Local                 Control).         -   If for any Listed system manager (e.g., one for which a             53—System Manager ID has become established) its             32—Connection Status is either undefined or “1” (connection             state 1—Listed), then             -   Test failed—(F6) Not all system managers have become                 configured.             -   Attribute 7—Node Status=“2” (state 2—Under Local                 Control).         -   If for any Listed asset (e.g., one for which a 2—Asset ID             has become established) its 32—Connection Status is either             undefined or “1” (connection state 1—Listed), then             -   Test failed—(F7) Not all assets have become configured.             -   Attribute 7—Node Status=“2” (state 2—Under Local                 Control).         -   If for any asset connection that has local information             connections listed in its 38—List of Local Information a             corresponding 52—Transactive Neighbor ID has not been             established, then             -   Test failed—(F8) Not all local information sources have                 been Listed.             -   Attribute 7—Node Status=“2” (state 2—Under Local                 Control).         -   If for any Listed local information connection (e.g., one             for which a 48—Local Information ID has become established)             its 32—Connection Status is either undefined or “1”             (connection state 1—Listed), then             -   Test failed—(F9) Not all local information connections                 have become configured.             -   Attribute 7—Node Status=“2” (state 2—Under Local                 Control).         -   If the Configuration Test fails to run to completion for any             other reason, then             -   Test failed—(F10) Unknown reasons.             -   Attribute 7—Node Status=“2” (state 2—Under Local                 Control)         -   Otherwise,             -   Test passed—(S2).             -   If prior 7—Node Status=“2” (state 2—Under Local                 Control), then Node Status=“3” (state 3—Configured).             -   Otherwise, Node Status should remain unchanged in the                 prior state.

Connection Test( )—this is neither a system management command nor an event, but it is a test of the completeness of the connections that should be completed between this transactive node and its connections. A Connection Test should be conducted automatically by a transactive node after a successful Configure( ) command and after any connection changes its connection state. A transactive node should have passed a Configuration Test before a Connection Test may be passed.

-   -   Parameters—None.     -   Test Logic         -   If upon checking attribute 7—Node Status this transactive             node is found to be in state “2” (2—Under Local Control),             then             -   Test failed—(F1) A transactive node should be Configured                 prior to a Connection Test.             -   No further tests are required.         -   If for any 52—Transactive Neighbor ID its 32—Connection             Status is other than “3” (connection state 3—Connected) or             “4” (connection state 4—Lost Connection), then             -   Test failed—(F2) Not all transactive neighbors are                 Connected.             -   Attribute 7—Node Status=“3” (state 3—Configured).         -   If for any 53—System Manager ID its 32—Connection Status is             other than “3” (connection state 3—Connected) or “4”             (connection state 4—Lost Connection), then             -   Test failed—(F3) Not all system managers are Connected.             -   Attribute 7—Node Status=“3” (state 3—Configured).         -   If for any 2—Asset ID its 32—Connection Status is other than             “3” (connection state 3—Connected) or “4” (connection state             4—Lost Connection), then             -   Test failed—(F4) Not all assets are Connected.             -   Attribute 7—Node Status=“3” (state 3—Configured).         -   If for any 26—Local Information ID its 32—Connection Status             is other than “3” (connection state 3—Connected) or “4”             (connection state 4—Lost Connection), then             -   Test failed—(F5) Not all local information connections                 are Connected.             -   Attribute 7—Node Status=“3” (state 3—Configured).         -   If the Connection Test fails to run to completion for any             other reason, then             -   Test failed—(F6) Unknown reason.             -   Attribute 7—Node Status=“3” (state 3—Configured).         -   Otherwise,             -   Test passed—(S1).             -   If prior 7—Node Status=“3” (state 3—Configured), then                 Node Status=“4” (state 4—Connected & Configured).             -   Otherwise, Node Status should remain unchanged in its                 prior state.

Operate( ) Command

-   -   Command Parameters—None.     -   Command Logic         -   The entity making the command should be found to be this             transactive node or one of its connections. If the entity             making this system management command is not the local             system manager and does not explicitly have this command             listed among the commands in its 31—Connection Partner's             System Management Permissions, then reply             -   “Command failed—(F1) Permissions do not include this                 command.”         -   If upon reviewing 7—Node Status the transactive node is             found to be in a state other than “4” (state 4—Connected &             Configured) or “5” (state 5—Operational), then reply             -   “Command failed—(F2) This command is not allowed from                 current state.”         -   If upon receiving this command this transactive node is not             able to enter or remain in state 5—Operational for any             reason, then reply             -   “Command failed—(F3) Unknown reason”         -   Otherwise,             -   Reply, “Command succeeded—(S1).”             -   Set 7—Node Status=“5” (state 5—Operational).             -   Begin interacting with transactive neighbor connections                 and other connections that are managed at this                 transactive node according to the algorithms of the                 toolkit framework and functions.

Handle Fatal Operational Event/Handle Non-Fatal Operational Event

-   -   The following error categories have been identified:         -   Application errors—an application error occurs within the             transactive control toolkit and may be due to faulty             software, logic or algorithms         -   Security and signal validation errors:—security and signal             validation errors are primarily associated with the incoming             TIS and TFS signals         -   Network errors—network errors are related to communications             network connectivity between transactive nodes.     -   Each error in these categories can further be classified as         transient (“Non-Fatal”) or permanent (“Fatal”).     -   A non-fatal error is an error where the system can recover from         the error without significant degradation of system         functionality and can therefore remain in the Operational state.         For example, if a transactive node does not receive a TIS signal         within the update interval (5 minutes for the Demonstration),         the TIS signal can be still be generated with minimal loss of         functionality (refer to the toolkit framework for how this is         accomplished). But if the TIS signal is not received for a         number of hours, then the transactive node may consider this a         fatal error and exit an Operational state. The function Handle         Non-Fatal Operational Event( ) has been provided within this         state model for the diagnostic recognition of and response to         non-fatal errors that will occur while the transactive node is         in an Operational state.

If a transient error happens often enough or lasts a long time it will turn into a fatal error. Fatal errors are, by definition, not recoverable and cause a transactive node to exit an Operational state. One of the two categories of fatal errors is due to a severe security, application, or network failure. A second category occurs when a non-fatal error is repeated “N” times in a row, or “K” times in an “M” minute interval depending on local policies. The function Handle Fatal Operational Event( ) has been provided within this state model for the diagnostic recognition of and response to fatal errors that may occur while the transactive node is in an Operational state.

The logic and details for these events remain to be worked out, but at this point the logic and details should be made to work within the state model that is being described here.

Halt Operations( ) Command

-   -   Command Parameters—None.     -   Command Logic         -   The entity making the command should be found to be this             transactive node or one of its connections. If the entity             making this system management command is not the local             system manager and does not explicitly have this command             listed among the commands in its 31—Connection Partner's             System Management Permissions, then reply             -   “Command failed—(F1) Permissions do not include this                 command.”         -   If upon reviewing 7—Node Status the transactive node is             found to be in a state other than “5” (state 5—Operational),             then reply             -   “Command succeeded—(S1) Operations already halted.”         -   Otherwise,             -   Reply, “Command succeeded—(S2).”             -   Set 7—Node Status=“4” (state 4—Connected & Configured).             -   Halt interacting with transactive neighbor connections                 and other connections that are managed at this                 transactive node according to the algorithms of the                 toolkit framework and functions.

Terminate Node( ) Command

-   -   Command Parameters—None.     -   Command Logic         -   If the entity that made this command is not the local system             manager and is not found to have been granted permission to             make this command by attribute 31—Connection Partner's             System Management Permissions, then reply             -   “Command failed—(F1) Lacking permission to make this                 command”         -   If upon checking 7—Node Status, this transactive node is             found to be in a state other than “2” (state 2—Under Local             Control) or “3” (state 3—Connected), then reply             -   “Command failed—(F2) Command not accepted in present                 transactive node state”         -   If the node executable fails to run for any other reason,             reply             -   “Command failed—(F3) Unknown reason”         -   Otherwise,             -   (Optional) Save a copy of the prior configuration. This                 configuration may be reloaded the next time a node                 executable is run to jump start the maturity of its                 configuration. This is the condition of the final state                 of this transactive node 1—New or Terminated.             -   Stop the node executable process from running.                 Attributes may become undefined by this action.             -   Reply, “Command succeeded—(S1)”.

6.1.8 Transactive Node State Transition Table

In the table below, the numbering convention used for these functions and events are concatenations of the prior and end states. Where multiple functions and events have identical prior and end states, letters have been appended. For example, “54b” is the number applied to the second of two transitions from state number 5 to state number 4.

TABLE 9 State Transition Table for a Transactive Node Acts Upon Producing Info. Internal Current Using To Set Next On the Gathered & Row Function State Input Attributes State Output Condition Recorded 11 Fail to Run 1 - New/ Filename 1 - New Reply: Failure - Command Node Terminated parameter Terminated “Command [(F1) File log entry Executable Source of failed - could not be command [(F1) File found/ Attributes could not (F3) Lacking 7 - Node be found/ permission to Status and (F3) Lacking make this 31 - Connection Permission command/ Partner's to make (F4) Critical System this transactive Management command/ node Permissions (F4) Critical attributes transactive were not node configured/ attributes (F5) were not Unknown configured/ reasons] (F5) Unknown reasons]” Command log entry 12 Run Node 1 - New/ Filename 1 - Node 2 - Under Reply: Success - Command Executable Terminated parameter ID, Local “Command (S1). log entry (starting Source of 5 - Node Control succeeded - Node state) command Version, (S1)” executable Attributes 7 - Node Action: runs. 7 - Node Status = Node Status and “2” (state executable 31 - Connection 2 - Under runs Partner's Local Command System Control), log entry Management and 18 - Permissions Time should be configured. Any and all remaining attributes may be set. 21 Terminate 2 - Under Source of All 1 - New/ Reply: Success - Command Node Local command attributes Terminated “Command (S1) log entry Control Attributes revert to (final succeeded - Node 7 - Node an state) (S1)” executable Status and undefined Action: successfully 31 - state Node terminated Connection and are executable Partner's lost when terminated System the node Command Management executable log entry Permissions is terminated. 22a Configuration 2 - Under 7 - Node 2 - Under Test log Failure - Test log Test Local Status, Local entry [(F1) The entry Failed Control 49 - List of Control transactive Transactive node is not Neighbors, configured/ 50 - List (F2) Not all of System transactive Managers, neighbors 51 - List have been of Assets, Listed/ 38 - List (F3) Not all of Local system Information, managers 52 - have been Transactive listed/ Neighbor (F4) Not all ID, 2 - assets have Asset ID, been listed/ 53 - (F5) Not all System transactive Manager neighbors ID, 48 - have become Local configured/ Information (F6) Not all ID, 32 - system Connection managers Status have become configured/ (F7) Not all assets have become configured/ (F8) Not all local information connections have been Listed/ (F9) Not all informations have become configured/ (F10) Unknown reasons] 22b Configure 2. Under Source of Node 2. Under Reply: Success - Command (Node Local command; attributes Local “Command (S1) log entry Attributes) Control Command- in the Control succeeded - line following (S1)” parameters; set may Command List of be set or log entry node modified attributes (e.g., that may “configured”): be {3, configured; 4, 8, 9, Attributes 16, 21, 7 - Node 22, 34, Status and 38, 39, 31 - Connection 40, 41, Partner's 42, 43, System 44, 45, Management 46, 47, Permissions; 49, 50 or referenced 51} configuration file 22c Connection 2 - Under 7 - Node 2 - Test log Test failed - Test log Test Failed Local Status, Under entry (F1) A entry Control 52 - Transactive Local transactive Neighbor Control node should ID, be 53 - System Configured Manager prior to a ID, 2 - Connection Asset ID, Test 48 - Local Information ID, 32 - Connection Status 22d Fail to 2 - Under Source of 2 - Under Reply: Failure - [(F1) Command Configure Local command; Local “Command Permissions log entry (Node Control Command- Control failed - do not Attributes) line [(F1) include this parameters; Permissions command/ List of do not (F3) File node include this cannot be attributes command/ found or that may (F3) File opened/ be cannot be (F4) Incorrect configured; found or node ID/ Attributes opened/ (F5) Command 7 - Node (F4) Incorrect did not Status and node ID/ address 31 - Connection (F5) Command known node Partner's did not attributes/ System address (F6) Management known Unknown Permissions; node reason] Referenced attributes/ Filename. (F6) Unknown reason]” Command log entry 22e Fail to 2 - Under Source of 2 - Reply: Failure - Command Operate Local command; Under “Command [(F1) Permissions log entry Control Attributes Local failed - do not 7 - Node Control [(F1) Permissions include this Status and do command/ 31 - Connection not include (F2) This Partner's this command is System command/ not allowed Management (F2) This from current Permissions command state] is not allowed from current state]” Command log entry 22f Fail to Halt 2 - Under Source of 2 - Reply: Failure - (F1) Command Operations Local command, Under “Command Permissions log entry Control Attributes Local failed - do not 7 - Node Control (F1) include this Status and Permissions commands 31 - Connection do not Partner's include this System command” Management Command Permissions log entry 22g Fail to Run 2 - Under Filename 2 - Reply: Failure - [(F1) Command Node Local parameter; Under “Command File could not log entry Executable Control Source Local failed - be found/ of Control [(F1) File (F2) Node command; could not executable is Attributes be found/ already 7 - Node (F2) Node running] Status and executable 31 - Connection is already Partner's running]” System Command Management log entry Permissions 22h Fail to 2 - Under Source of 2 - Under Reply: Failure - [(F1) Command Terminate Local command; Local “Command Lacking log entry Node Control Attributes Control failed - permission to 7 - Node [(F1) make this Status and Lacking command/ 31 - permission (F3) Connection to make Unknown Partner's this reason] System command/ Management (F3) Permissions Unknown reason]” Command log entry 22i Halt 2 - Under Source of 2 - Reply: Success - Command Operations Local command; Under “Command (S1) log entry Control Attributes Local succeeded - 7 - Node Control (S1)” Status and Command 31 - Connection log entry Partner's System Management Permissions 23 Configuration 2 - Under Attributes 7 - Node 3 - Configured Test log Pass Test log on Test Local 7 - Node Status = entry condition entry Passed Control Status, “3” (state (S2). See test (condition 49 - List of 3 - Configured) logic. (S2)) Transactive Transactive Neighbors, node 50 - List configuration of System is complete Managers, and internally 51 - List consistent. of Assets, 38 - List of Local Information, 52 - Transactive Neighbor ID, 2 - Asset ID, 53 - System Manager ID, 48 - Local Information ID, 32 - Connection Status 31 Terminate 3 - Configured Source of All 1 - New/ Reply: Success - Command Node command; attributes Terminated “Command (S1) log entry Attributes revert to (final succeeded - Node 7 - Node an state) (S1)” executable is Status and undefined Action: terminated. 31 - Connection state Node Partner's and are executable System lost when stops Management the node Command Permissions executable log entry is terminated. 32 Configuration 3 - Configured Attributes 7 - Node 2 - Under Test log Failure - Test log Test 7 - Node Status = Local entry [(F1) The entry Failed Status, “2” (state Control transactive 49 - List of 2 - node is not Transactive Under configured/ Neighbors, Local (F2) Not all 50 - List Control) transactive of System neighbors Managers, have been 51 - List Listed/ of Assets, (F3) Not all 38 - List system of Local managers Information, have been 52 - Listed/ Transactive (F4) Not all Neighbor assets have ID, 2 - been Listed/ Asset ID, (F5) Not all 53 - transactive System neighbors Manager have become ID, 48 - configured/ Local (F6) Not all Information system ID, 32 - managers Connection have become Status configured/ (F7) Not all assets have become configured/ (F8) Not all local information connections have been Listed/ (F9) Not all information connections have become configured/ (F10) Unknown reasons] 33a Configuration 3 - Configured Attributes 3 - Configured Test log Pass Test log Test 7 - Node entry condition entry Passed Status, (S2). See test (condition 49 - List of logic. (S2) Transactive Transactive Neighbors, node 50 - List configuration of System is complete Managers, and internally 51 - List consistent. of Assets, 38 - List of Local Information, 52 - Transactive Neighbor ID, 2 - Asset ID, 53 - System Manager ID, 48 - Local Information ID, 32 - Connection Status 33b Configure 3 - Configured Source of Node 3 - Configured Reply: Success - Command (Node command; attributes “Command (S1) log entry Attributes) Command- in the succeeded - line following (S1)” parameters; set may Command List of be set or log entry node modified attributes (e.g., that may “configured”): be {3, configured; 4, 8, 9, Attributes 16, 21, 7 - Node 22, 34, Status and 38, 39, 31 - Connection 40, 41, Partner's 42, 43, System 44, 45, Management 46, 47, Permissions; 49, 50 or Referenced 51} configuration file Filename 33c Connection 3 - Configured Attributes 3 - Configured Test log Test failed - Test log Test Failed 7 - Node entry [(F2) Not all entry Status, transactive 52 - Transactive neighbors are Neighbor Connected/ ID, (F3) Not all 53 - System system Manager managers are ID, 2 - Connected/ Asset ID, (F4) Not all 48 - Local assets are Information Connected/ ID, (F5) Not all 32 - Connection local Status information connections are Connected/ (F6) Unknown reason] 33d Fail to 3 - Configured Source of 3 - Configured Reply: Failure - [(F1) Command Configure command; “Command Permissions log entry (Node Command- failed - do not Attributes) line [(F1) include this parameters; Permissions command/ List of do not (F3) File node include this cannot be attributes command/ found or that may (F3) File opened/ be cannot be (F4) Incorrect configured; found or node ID/ Attributes opened/ (F5) Command 7 - Node (F4) Incorrect did not Status and node ID/ address 31 - Connection (F5) Command known node Partner's did not attributes/ System address (F6) Management known Unknown Permissions; node reason] referenced attributes/ configuration (F6) file Unknown reason]” Command log entry 33e Fail to 3 - Configured Source of 3 - Configured Reply: Failure - Command Operate command; “Command [(F1) Permissions log entry Attributes failed - do not 7 - Node [(F1) Permissions include this Status and do command/ 31 - Connection not include (F2) This Partner's this command is System command/ not allowed Management (F2) This from current Permissions command state] is not allowed from current state]” Command log entry 33f Fail to Halt 3 - Configured Source of 3 - Configured Reply: Failure - (F1) Command Operations command, “Command Permissions log entry Attributes failed - do not 7 - Node (F1) include this Status and Permissions command 31 - Connection do not Partner's include this System command” Management Command Permissions log entry 33g Fail to Run 3 - Configured Filename 3 - Configured Reply: Failure - Command Node parameter; “Command [(F1)) File log entry Executable Source failed - could not be of [(F1)) File found/(F2) command; could not Node Attributes be found/ executable is 7 - Node (F2) Node already Status and executable running] 31 - Connection is already Partner's running]” System Command Management log entry Permissions 33h Fail to 3 - Configured Source of 3 - Configured Reply: Failure - [(F1) Command Terminate command; “Command Lacking log entry Node Attributes failed - permission to 7 - Node [(F1) make this Status and Lacking command/ 31 - permission (F3) Connection to make Unknown Partner's this reason] System command/ Management (F3) Permissions Unknown reason]” Command log entry 33i Halt 3 - Configured Source of 3 - Configured Reply: Success - Command Operations command; “Command (S1) log entry Attributes succeeded - 7 - Node (S1)” Status and Command 31 - Connection log entry Partner's System Management Permissions 34 Connection 3 - Configured Attributes 7 - Node 4 - Connected & Test log Success - Test log Test 7 - Node Status = Configured entry (S1). entry Passed Status, “4” (state Set of 52 - Transactive 4 - connections Neighbor Connected & is complete ID, Configured) and 53 - System connected Manager ID, 2 - Asset ID, 48 - Local Information ID, 32 - Connection Status 42 Configuration 4 - Attributes 7 - Node 2 - Under Test log Failure - Test log Test Connected & 7 - Node Status = Local entry [(F1) The entry Failed Configured Status, “2” (state Control transactive 49 - List of 2 - node is not Transactive Under configured/ Neighbors, Local (F2) Not all 50 - List Control) transactive of System neighbors Managers, have been 51 - List Listed/ of Assets, (F3) Not all 38 - List system of Local managers Information, have been 52 - Listed/ Transactive (F4) Not all Neighbor assets have ID, 2 - been Listed/ Asset ID, (F5) Not all 53 - transactive System neighbors Manager have become ID, 48 - configured/ Local (F6) Not all Information system ID, 32 - managers Connection have become Status configured/ (F7) Not all assets have become configured/ (F8) Not all local information sources have been Listed/ (F9) Not all information sourcess have become configured/ (F10) Unknown reasons] 43 Connection 4 - Connected & Attributes 7 - Node 3 - Configured Test log Test failed - Test log Test Failed Configured 7 - Node Status = entry [(F2) Not all entry Status, “3” (state transactive 52 - Transactive 3 - Configured) neighbors are Neighbor connected/ ID, (F3) Not all 53 - System system Manager managers are ID, 2 - connected/ Asset ID, (F4) Not all 48 - Local assets are Information connected/ ID, (F5) Not all 32 - Connection local Status information sources are Connected/ (F6) Unknown reason] 44a Configuration 4 - Attributes 4 - Test log Pass Test log Test Connected & 7 - Node Connected & entry condition entry Passed Configured Status, Configured (S2). See test (condition 49 - List of logic. (S2) Transactive Transactive Neighbors, node 50 - List configuration of System is complete Managers, and internally 51 - List consistent. of Assets, 38 - List of Local Information, 52 - Transactive Neighbor ID, 2 - Asset ID, 53 - System Manager ID, 48 - Local Information ID, 32 - Connection Status 44b Configure 4 - Source of Node 4 - Reply: Success - Command (Node Connected & command; attributes Connected & “Command (S1) log entry Attributes) Configured Command- in the Configured succeeded - See line following (S1)” command parameters; set may Command logic List of be set or log entry node modified attributes (e.g., that may “configured”): be {3, configured; 4, 8, 9, Attributes 16, 21, 7 - Node 22, 34, Status and 38, 39, 31 - Connection 40, 41, Partner's 42, 43, System 44, 45, Management 46, 47, Permissions; 49, 50 or Referenced 51} configuration file Filename 44c Connection 4 - Attributes 4 - Connected & Test log Success - Test log Test Connected & 7 - Node Configured entry (S1) entry Passed Configured Status, All 52 - Transactive connections Neighbor are complete ID, and 53 - System connected. Manager ID, 2 - Asset ID, 48 - Local Information ID, 32 - Connection Status 44d Fail to 4 - Connected & Source of 4 - Connected & Reply: Failure - [(F1) Command Configure Configured command; Configured “Command Permissions log entry (Node Command- failed - do not Attributes) line [(F1) include this parameters; Permissions command/ List of do not (F3) File node include this cannot be attributes command/ found or that may (F3) File opened/ be cannot be (F4) Incorrect configured; found or node ID/ Attributes opened/ (F5) Command 7 - Node (F4) Incorrect did not Status and node ID/ address 31 - Connection (F5) Command known node Partner's did not attributes/ System address (F6) Management known Unknown Permissions; node reason] referenced attributes/ configuration (F6) file Unknown reason]” Command log entry 44e Fail to 4 - Connected & Source of 4 - Connected & Reply: Failure - Command Operate Configured command; Configured “Command [(F1) Permissions log entry Attributes failed - do not 7 - Node [(F1) Permissions include this Status and do command/ 31 - Connection not include (F3) Unknown Partner's this reason] System command/ Management (F3) Unknown Permissions reason]” Command log entry 44f Fail to Halt 4 - Connected & Source of 4 - Connected & Reply: Failure - (F1) Command Operations Configured command, Configured “Command Permissions log entry Attributes failed - do not 7 - Node (F1) include this Status and Permissions command 31 - Connection do not Partner's include this System command.” Management Command Permissions log entry 44g Fail to Run 4 - Filename 4 - Reply: Failure - [(F1) Command Node Connected & parameter; Connected & “Command File could not log entry Executable Configured Source Configured failed - be found/ of [(F1) File (F2) Node command; could not executable is Attributes be found/ already 7 - Node (F2) Node running] Status and executable 31 - Connection is already Partner's running]” System Command Management log entry Permissions 44h Fail to 4 - Source of 4 - Reply: Failure - [(F1) Command Terminate Connected & command; Connected & “Command Lacking log entry Node Configured Attributes Configured failed - permission to 7 - Node [(F1) make this Status and Lacking command/ 31 - permission (F2) Connection to make Command Partner's this not accepted System command/ in present Management (F2) transactive Permissions Command node state]” not accepted in present transactive node state]” Command log entry 44i Halt 4 - Source of 4 - Reply: Success - Command Operations Connected & command, Connected & “Command (S1) log entry Configured Attributes Configured succeeded - 7 - Node (S1)” Status and Command 31 - Connection log entry Partner's System Management Permissions 45 Operate 4 - Connected & Source of 7 - Node 5 - Operational Reply: Success - Command Configured command; Status = “Command (S1) log entry Attributes “5” (state succeeded - 7 - Node 5 - (S1)” Status and Operational) Action: 31 - Connection Transactive Partner's node System begins Management interacting Permissions with transactive control and coordination system Command log entry 53 Connection 5 - Operational Attributes 7 - Node 3 - Configured Test log Test failed - Test log Test Failed 7 - Node Status = entry [(F2) Not all entry Status, “3” (state transactive 52 - Transactive 3 - Configured) neighbors are Neighbor Connected/ ID, (F3) Not all 53 - System system Manager managers are ID, 2 - Connected/ Asset ID, (F4) Not all 48 - Local assets are Information Connected/ ID, (F5) Not all 32 - Connection local Status information sources are Connected/ (F6) Unknown reason] 54a Handle 5 - Operational Diagnostic 7 - Node 4 - Connected & Notifications Non- Event log Fatal recognition Status = Configured TBD recoverable entry Operational of Fatal “4” (state Event log error during Event Operational 4 - entry transactive Event Connected & node Details Configured) operations TBD 54b Halt 5 - Operational Source of 7 - Node 4 - Connected & Reply: Success - Command Operations command; Status = Configured “Command (S2) log entry Attributes “4” (state succeeded - 7 - Node 4 - (S2)” Status and Connected & Action: The 31 - Connection Configured) transactive Partner's node halts System its Management interactions Permissions with the transactive control and coordination system Command log entry 55a Configuration 5 - Operational Attributes 5 - Operational Test log Pass Test log Test 7 - Node entry condition entry Passed Status, (S1). See test (condition 49 - List of logic. (S1) Transactive Transactive Neighbors, node 50 - List configuration of System is complete Managers, and internally 51 - List consistent of Assets, 38 - List of Local Information, 52 - Transactive Neighbor ID, 2 - Asset ID, 53 - System Manager ID, 48 - Local Information ID, 32 - Connection Status 55b Connection 5 - Operational Attributes 5 - Operational Test log Success - Test log Test 7 - Node entry (S1) entry Passed Status, All 52 - Transactive connections Neighbor are complete ID, and 53 - System connected. Manager ID, 2 - Asset ID, 48 - Local Information ID, 32 - Connection Status 55c Fail to 5 - Operational Source of 5 - Operational Reply: Failure - [(F1) Command Configure command; “Command Permissions log entry (Node Command- failed - do not Attributes) line [(F1) include this parameters; Permissions command/ List of do not (F2) Configure node include this command attributes command/ not allowed that may (F2) Configure from be command Operational configured; not allowed state] Attributes from 7 - Node Operational Status and state]” 31 - Connection Command Partner's log entry System Management Permissions; Referenced configuration file Filename 55d Fail to Halt 5 - Operational Source of 5 - Operational Reply: Failure - (F1) Command Operations command, “Command Permissions log entry Attributes failed - do not 7 - Node (F1) include this Status and Permissions command 31 - Connection do not Partner's include this System command” Management Command Permissions log entry 55e Fail to Run 5 - Operational Filename 5 - Operational Reply: Failure - [(F1) Command Node parameter; “Command File could not log entry Executable Source failed - be found/ of [(F1) File (F2) Node command; could not executable is Attributes be found/ already 7 - Node (F2) Node running] Status and executable 31 - Connection is already Partner's running]” System Command Management log entry Permissions 55f Fail to 5 - Operational Source of 5 - Operational Reply: Failure - [(F1) Command Terminate command; “Command Lacking log entry Node Attributes failed - permission to 7 - Node [(F1) make this Status and Lacking command/ 31 - permission (F2] Connection to make Command Partner's this not accepted System command/ in present Management (F2] transactive Permissions Command node state] not accepted in present transactive node state]” Command log entry 55h Operate 5 - Operational Source of 5 - Operational Reply: Success - Command command; “Command (S1) log entry Attributes succeeded - 7 - Node (S1)” Status and Command 31 - Connection log entry Partner's System Management Permissions

6.1.9 Connection Attributes

Connection attributes have been identified and are ascribable in common to the four types of connections. This set of attributes refers to a single connection between this transactive node and a transactive neighbor, system manager, asset system, or source of local information. The connection attributes are indispensible for keeping track of the state of any type of connection. It is never adequate to reference these attributes apart from a specific example of attribute 27—Connection ID.

Connection attributes are important for navigating the connection state transition diagram 3000 in FIG. 30. The attribute 32—Connection Status should be known and managed for each connection. Attribute 7—Node Status has been shown to be a logical combination of multiple individual Connection Statuses.

Refer to Table 11 for the anticipated ways in which the connection attributes may be affected by the commands and events of the connection state model.

In the connection state model (see FIG. 30), a connection moves between its states by undergoing Configuration Tests, accepting Connect and Disconnect commands, and experiencing some events like Loss of Connection. Important connection attribute 32—Connection Status keeps track of these state changes. For example, a local information connection transitions into state Connection Status “2” (connection state 2—Configured) if connection attribute 32—Connection Status and the local information attribute 48—Connection Status have been configured. (The sets of attributes that should be configured before a connection may enter connection state 2—Configured are indicated conveniently by asterisks in FIG. 28.)

TABLE 10 Dictionary of Connection Attributes that should be applied to each Connection Attribute No. Name Description Role Type Format Range of values 32 Connection Indicates the Affected by Integer Example: “2” 1 -Listed, Status* state of the Connect( ) 2 - Configured, connection command. 3 - Connected, between this A transactive 4 - Lost transactive node Connection node and a conducts a connection. Connection Test based on the Connection Statuses of its Connection 29 Connection An indicator May be used Character Example: “RL”—Responsive Partner of type of to indicate string “SM” Load Type* connection applicable partner from interactions “OL”—Other a list of and Local allowed permissions. Condition partner types For example, Input to include at transactive “OS”—Owner least neighbors or transactive expect to Subsystem neighbors, receive and “RR”—Responsive owner. supply Rsource transactive signals. “SM”—System System Manager managers “TN”—Transactive should be Neighbor granted some system management permissions. 17 Connection Optional Each List of Detail1, A list of Details additional connection alphanumeric detail2, . . . necessary details about method used is strings details should the in attribute be created for connection 33 - Connection each method on Method connection stated in should method of attribute prescribe a attribute 33. For 33 - Connection set of details example, on Method that should Internet (IP for a be provided address of this connection. by this transactive attribute. node, IP address of connection partner, encryption level, . . . ) 28 Connection's The locations Support Most likely (latitude, 0-360 Geographical of connection future GIS a pair of longitude) As degrees; Location partners system real for attribute 0-2 * pi radians should be representations numbers #4. provided to representing Geographical identify map angular Location, locations to latitude and angular which this longitude. latitude and transactive longitude are node has the default established units. connections. Standard This attribute GIS is optional for representation each formats connection. should be adapted if such standards can be identified. 30 Entities For each Eventually, List of Use If null, only the Permitted specified the alphanumeric guidance local transactive to Modify connection, a transactive identifiers, provided with node system this list of those nodes will one for 1 - Node ID manager may Connection entities that operate with each entity and modify the are permitted considerable that will be 28 - Connection specified to initiate, autonomy granted this ID. Use connection. modify, or and should permission formats disconnect clearly for this found in the specify connection. transactive connection which, if any control and and its connection coordination attributes. partners may system This list may modify topology narrow the connections. maps. permissions The granted to a Demonstration connection has many partner by instances attribute where a utility 31 - Connection owner should Partner be granted Permissions. permissions to modify a transactive node's connections. 31 Connection The general This attribute List of List of Entries selected Partner's permissions allows system allowed from System granted to system management system {Configure([All, Management connection management commands management 1, 2, . . . ]), Permissions partners to responsibilities that will be commands Connect, issue system to be accepted {command1, Disconnect, management assigned to from a command2, Operate, Run commands at one or more connection . . . }. If the list Node this of the partner at includes Executable, transactive connection this command Stop, Terminate node, plus partners at transactive Configure( ), Node} the this node, plus the list of If null, then only transactive transactive list of modifiable this transactive node node. transactive attributes node's system attributes Assigned node should be manager may that may be among attributes listed as issue system modified by Connection that may be parameters management the Table modified by of this commands. connection attributes. this command by partner See connection number. during Connection partner. configuration. Table. These permissions may be restricted further by attribute 30 - Entities Permitted to Modify this Connection. 33 Connection Optional Specify the Single Example: Ethernet, Method indication of method of character “Internet” Internet, the media connection. string Wireless and protocol Each such Zigbee ®, used in a method may Wireless other, connection. then have Power Line specific Carrier details to be listed in attribute 34 - Connection Details. 54 Connection The period of This is the Character Recommend The Timeout time that a amount of string “dd:hh:mm”. Demonstration Period given time that representation Should should use connection should of a emulate UTC values longer will remain in elapse before single time standard than 5 minutes its Lost a connection duration format that is “00:00:05” or Connection in its Lost used shorter than 4 state before Connection frequently in days “04:00:00”. it will state will state model. Default value: 1 terminate the automatically hour: connection, transition “00:01:00”. which could back into its threaten the Configured Operational state. This status of this duration may transactive be quite long node. if this transactive node and its algorithms have been designed tolerant of poor connectivity. This timeout period is to be individually configured for each connection. 55 Loss of A list of times By keeping List of UTC See UTC Allow for cyclic Connection at which track of when times. standard buffer of 64 on Event Loss of Loss of values. Buffer Connection Connection Need not be Events have Events occur, initialized. occurred for a transactive a given node can connection. take exceptional actions based on the frequency with which the events have occurred. 56 Allowed The Criteria Two Example: (5, Default (6, 48), Frequency frequency placed on the integers. 24), meaning meaning six of Loss with which members of 5 times in an times in an of Loss of 55 - Loss of hour, or 24 hour, or 48 Connection Connection Connection times in a times in during Events Events will Event Buffer. day a day. Integers be tolerated The should be less before the connection than the buffer connection should be length of will be severed if 55 - Loss of severed. these Connection There is a frequencies Event Buffer. criterion for are events per exceeded, hour and which would another for indicate a events per problem with day. the connection.

TABLE 11 The Ways Connection Attributes May be Affected by Connection State Model Commands and Events

Configure Configuration Connect Disconnect Connection Connection Connection Attribute # Attribute Name Command Test** Command Command Event Event Event 32 Connection Status* +0 C + 0 C0 C0 C00 C00 C00 29 Connection Partner Type* +0 C (C) 30 Entities Permitted to C + 0 C Modify this Connection 31 Connection Partner's C + 0 C C System Management Permissions 17 Connection Details +0 (C) (C) (C) (C) 28 Connection's Geographical +0 Location 33 Connection Method +0 (C) (C) (C) (C) (C) 54 Connection Timeout Period +0 C C C 55 Loss of Connection +0 C + 0 Event Buffer 56 Allowed Frequency of +0 C Loss of Connection Events *The Connection Status should be configured before a connection can enter its 2 - Configured state. **The connection Configuration Test additionally should check one or more attributes of the connection partner type.

indicates data missing or illegible when filed

6.1.10 Transactive Neighbor Connection Attributes

In certain embodiments, transactive node define at least one connection to a transactive neighbor. The connection may be observed and maintained using the union of connection attributes and transactive node attributes (see FIG. 28).

At least for some of the connections that are being made to transactive neighbors, it may be desired that experimenters and testing entities have the means to redirect the inputs received from the transactive neighbors so that these inputs may be received instead from selected alternative sources of such information. It is likewise important that one may redirect the output from these connection partners to one or more alternative locations. For the special type of connection partners called transactive neighbors, the means to redirect inputs and outputs has been accomplished with attributes 10-13, which attributes define the sources and targets of transactive signals. The sources and targets are not necessarily the transactive neighbor itself. Using these attributes, simulations and “what-if” scenarios may be designed and tested in the production or test system environments. (So far, attributes #10-13 only apply to transactive neighbors and their connections. It is conceivable that the attributes could be generalized and renamed to apply to any connection type, not only transactive neighbors.)

TABLE 12 Dictionary of Transactive Neighbor Attributes Attribute No. Name Description Role Type Format Range of values 52 Transactive The This asset Single character Example #1: See system Neighbor identifier to should be string “UT06”, which is the topology. ID* be used for repeated for Demonstration's Naming practice one each identifier for the should be the transactive member of Avista utility. same here and for neighbor 49 - List of attribute 49 - List with which Transactive of Transactive this Neighbors Neighbors. transactive to node will instantiate exchange the electrical transactive energy and neighbors therefore that this will transactive exchange node transactive expects to signals. interact with. This transactive neighbor enters its Listed state after this attribute has been configured. 10 Receive TIS The This Single, short Use guidance Source should be Source* Connection attribute alphanumeric provided with a known source ID of a permits identifier for each 1 - Node ID and within present source from alternative transactive 28 - Connection ID. transactive control which a TIS neighbor. Use formats found and coordination transactive examples to in transactive system. neighbor's be received control and TIS should at this coordination system be transactive topology maps. received. node from The source alternative is not sources to necessarily facilitate the testing and transactive simulation. neighbor itself. 11 Receive The This Single, short Use guidance Source should be TFS Connection attribute alphanumeric provided with a known source Source* ID of a permits identifier for each 1 - Node ID and within present source from alternative transactive 28 - Connection ID. transactive control which a TFS neighbor Use formats found and coordination transactive examples to in transactive system. neighbor's be received control and TFS should at this coordination system be transactive topology maps. received. node to The source facilitate is not the testing and transactive simulation. neighbor itself. 12 Send TIS The This List of one or Use guidance Target should be Targets* Connection attribute many single provided with known location ID of at permits this short 1 - Node ID and within present least one transactive alphanumeric 28 - Connection ID. transactive control target node's TIS identifiers for Use formats found and coordination location to to be sent to each transactive in transactive system. which this one or more neighbor control and transactive places to coordination system node's TIS facilitate topology maps. should be testing and sent. The simulation. target location is not necessarily that of the transactive neighbor itself. 13 Send TFS The This List of one or Use guidance Target should be Targets* Connection attribute many single provided with known location ID of at permits this short 1 - Node ID and within present least one transactive alphanumeric 28 - Connection ID. transactive control target node's TFS identifiers for Use formats found and coordination location to for this each transactive in transactive system. which this transactive neighbor control and transactive neighbor to coordination system node's be sent to topology maps. calculated one or more TFS with places to this facilitate transactive testing and neighbor simulation. should be sent. The target location is not necessarily that of the transactive neighbor itself. 23 Received Contains at Each List of TIS According to See range TIS Buffer least the transactive transactive signal attributes of TIS most recent neighbor's format as defined TIS TIS is used by approved XML messages within the schema for the TIS. received toolkit from each framework transactive algorithms. neighbor. To be stored to the Input Transactive Signal Buffer of the toolkit framework. 24 Received Contains at Each List of TFS According to See range TFS Buffer least the transactive transactive signal attributes of the most recent neighbor's format as defined TFS TFS TFS is used by approved XML messages within the schema for the received toolkit TFS. from each framework transactive algorithms. neighbor. To be stored to the Input Transactive Signal Buffer of the toolkit framework. 59 TIS Sent Flag that is This flag Boolean Boolean logic. 0 - default value - Flag set if a TIS may be condition flag: cleared - no TIS has been used in 0 - cleared has been transmitted conjunction 1 - set transmitted to this to this with the transactive transactive watchdog neighbor yet neighbor timer. The during the current connection actions update interval. by this taken upon 1 - set - a TIS transactive a watchdog has been node during timer event transmitted to this the current may transactive update desirably neighbor during interval. have the the current update The flag is transactive interval. cleared at node keep the track of to beginning which of each transactive update neighbor interval. transactive signals have been transmitted and not. 60 TFS Sent Flag that is This flag Boolean Boolean logic. 0 - default value - Flag set if a TFS may be condition flag: cleared - no has been used in set/cleared. TFS has been transmitted conjunction transmitted to this to this with the transactive transactive watchdog neighbor yet neighbor by timer. The during the current this actions update interval. transactive taken upon 1 - set - a TFS node during a watchdog has been the current timer event transmitted to this update may transactive interval. desirably neighbor during The flag is have the the current update cleared at transactive interval. the node keep beginning track of to of each which update transactive interval. neighbor transactive signals have been transmitted and not. *This attributes should be configured to pass a connection Configuration Test.

TABLE 13 The Ways Transactive Neighbor Attributes May be Affected by Connection State Model Commands and Events Loss of Re-Establish Terminate Configure Configuration Connect Disconnect Connection Connection Connection Attribute # Attribute Name Command Test** Command Command Event Event Event 52 Transactive Neighbor (C) + 0 C (C) (C) (C) (C) (C) ID* 10 Receive TIS Source* +0 C (C) (C) (C) (C) (C) 11 Receive TFS Source* +0 C (C) C) (C) (C) (C) 12 Sent TIS Targets* +0 C (C) (C) (C) (C) (C) 13 Send TFS Targets* +0 C (C) (C) (C) (C) (C) 23 Received TIS Buffer +0 24 Received TFS Buffer +0 *These attributes should be configured before a transactive neighbor connection can enter its 2 - Configured state. **The connection Configuration Test additionally should check that 32 - Connection Status has been configured.

6.1.11 System Manager Connection Attributes

In certain embodiments, a single attribute can define a connection to a system manager.

TABLE 14 Ways in which System Manager Connection Attributes may be affected by Connection Commands and Events Loss of Re-Establish Terminate Configure Configuration Connect Disconnect Connection Connection Connection Attribute # Attribute Name Command Test** Command Command Event Event Event 52 System Manager (C) + 0 C (C) (C) (C) (C) (C) ID* *The Connection Status should be configured before a connection can enter its 2 - Configured state. **The connection Configuration Test additionally should check one or more attributes of the connection partner type.

Note that in certain implementations, transactive nodes establish and maintain a connection to the global system manager. Therefore, attribute 52 System Manager ID includes the ID of this global system manager for the transactive nodes.

TABLE 15 Dictionary of System Manager Connection Attributes Attribute Range of No. Name Description Role Type Format values 52 System The identifier This attribute Single Example #1: See system Manager for one system instantiates a character “EI01” to topology. ID* manager. This system manager strings represent the Naming entity will be from those that system practice granted appear in 50 - List manager, from should be permissions by of System which system the same this transactive Managers. A system management here and for node to make manager for which command will attribute some system this attribute has likely be 50 - List of management been configured will received. System commands. enter its Listed state. Managers. A system manager is not a transactive neighbor, but a transactive neighbor may be granted permissions to act as a system manager. This transactive node may instantiate multiple connections to system managers. The Demonstration, for example, will have some central system management (“EI01”), but this transactive node may also grant system administration rights to the utility that “owns” this transactive node.

6.1.12 Asset Connection Attributes

This group of Asset attributes are meaningful only in respect to a given connection to an asset, which can be an energy resource, an incentive, or a load. Each resource or incentive has a corresponding toolkit resource and incentive function that defines how its behavior and effects may be modeled or predicted for the formulation of the transactive signals according to the toolkit framework. Each load similarly should have a corresponding toolkit load function that describes its effect on the formulation of the TFS. Often these “assets” will, in fact, be rather complex systems of assets.

An asset connection may list a set of local information connections that should be established via its 38—List of Local Information. Each member of this list creates an expectation that a local information connection will become established.

An asset connection should have its 2—Asset ID, 6—Toolkit Function, and 6—Asset Type configured before it is able to enter into the connection state 2—Configured

TABLE 16 Dictionary of Asset Connection Attributes Attribute Range of No. Name Description Role Type Format values 2 Asset ID* This attribute Each Character Recommend The identifies the resource, string format “XX- Demonstration resources, incentive, or #” for each has already incentives, load should be asset, where specified and loads identified “XX” is a 2- identifiers for associated along with its letter responsive with this toolkit acronym for asset systems transactive function, the owner of (most of which control node. status, this are loads) predicted/ transactive according to scheduled node, and “#’ this engagement, is an integer convention. etc. number that Loads = [0-999] ensures that Resources = the identifier [1000-1999] is unique. Incentives = [2000-2999] 6 Toolkit An States the List of {filename1, Valid Function identification specific toolkit Alphanumeric #.##; filenames are ID* of the function and modules or filename2, to be used. specific version that is filenames and #.##, . . . } “#.##” is major toolkit being applied the present and minor function and at this version of version version-the transactive these numbers using functional node for each modules. digits 0-9. algorithms resource, used at this incentive, or transactive load. node to A toolkit process the function TIS, TFS, should be local named for information, each and to resource, control incentive, and associated load for which assets. predictive behavior is being modeled by a toolkit function. 25 Asset Enables a This feature List of See 2 - Should refer to Output control may facilitate alphanumeric Asset ID. valid resource Targets* output to a using the identifiers or load entity resource or installed ID from the load to transactive 2 - Asset IDs become control and that are being redirected to coordination used. one or more system for target simulation of locations. asset The target responses locations do under not alternative necessarily scenarios and include the during testing. resource or If targets do load itself. not include the asset system, the asset should not respond. Should be configured for a successful connection Configuration Test. 36 Asset Type Declaration May be useful Single Example: “Resource”—describes of asset type for alphanumeric “Resource” a at least from categorization string generator among of asset resource at “Resource,” connections. this transactive “Incentive,” Range of node. and “Load.” values may be “Incentive”—describes expanded. an See the toolkit incentive that framework to is not a understand resource. the roles of “Load”—describes toolkit an functions. elastic or inelastic load at this transactive node. 38 List of Local A list of the An asset's List of See 48 - Should Information sources of predicted character Local correspond to Connections local behavior is strings Information valid 48 - information modeled by a ID. Local that will be toolkit Information ID. called upon function, to help which in turn predict the may call upon behavior of sources of this asset. local information. A connection listed in this attribute creates an expectation that this transactive node will establish and manage the connection.

To support future simulations and testing, the connection state model includes an ability to redirect the output of these asset connections. Some of the assets will be responsive to the transactive control and coordination system and an output “control” signal is sent to these asset systems by this transactive node. Attribute 25—Asset Output Targets allows the targets of these “control” signals to be sent to the asset system, to another entity, or to both the asset system the other entity.

List the local information inputs that are anticipated by an asset system and the toolkit function that predicts its behaviors. These streams of input information that are at time referred to as “other local conditions” should additionally become listed as attributes 48—Local Information ID so that the continuity of the data stream may be monitored and so the input can become redirected, thus allowing alternative scenarios to be simulated with alternative input information.

Table 17 lists the asset attributes and indicates how these attributes may be affected by the system management commands and events that are part of the connection state model.

TABLE 17 The Ways Asset Attributes May be Affected by Connection State Model Commands and Events

Configure Configuration Connect Disconnect Connection Connection Connection Attribute # Attribute Name Command Test** Command Command Event Event Event 2 Asset ID* C + 0 C (C) (C) (C) (C) (C) 6 Toolkit Function* +0 C 25 Asset Output Targets* (C) + 0 C (C) (C) (C) (C) (C) 36 Asset Type +0 (C) 38 List of Local Information (C) + 0 C (C) (C) (C) (C) (C) Connections *These attributes should be configured before an asset connection can enter its 2 - Configured state. **The connection Configuration Test additionally should check that 32 - Connection Status has been configured.

indicates data missing or illegible when filed

The Assets in the Asset Table of Table 16 are closely aligned with several of the interim data storage areas (“buffers”) that have been defined in the toolkit framework and with appear also in the state mode. For an asset connection there should be corresponding entries in one or more of the buffer (storage) areas that have been defined in the toolkit framework:

-   -   Resource entries necessitate updating one record in attribute         34—Resource Schedule and Cost Buffer during each iteration at         the update frequency. (An exception may occur because an option         has been provided for resource schedules to be entered without         corresponding toolkit functions. This might be selected for some         resources that are dispatched entirely unaffected by transactive         control.) For a resource, this entry will state at least an         energy parameter and average power produced by the corresponding         resource for each interval start time.     -   Incentive entries, like resources, also necessitate updating one         record in attribute 34—Resource Schedule and Cost Buffer during         each iteration at the update frequency. That entry will include         entries from among a paired set of capacity factor and capacity,         an infrastructure parameter, and another costs parameter.     -   Load entries necessitate one record be made in each attribute         45—Inelastic Load Prediction Buffer and 46—Elastic Load         Prediction Buffer each iteration. The entries in those buffer         (storage) locations predict load and, for responsive assets, the         predicted level of engagement of responsive asset systems.

6.1.13 Local Information Connection Attributes

TABLE 18 Dictionary of Local Information Connection Attributes Attribute Range of No. Name Description Role Type Format values 48 Local Unique This ID should Single Recommend “XX- Should match Information identifier to be listed in a character OLC-3###”, formats and ID* keep track of record of the string. where XX is an entries in the local Connections acronym for the 38 - List of information Table. node owner, Local that are used Once clearly “3###” is a Information by this identified, this number from Connections transactive input may 3000 to 3999. node. then be Example: “AV- “Local supplied by OLC-3001” Information” alternative has been sources via referred to as the attribute “Other Local 26 - Local Conditions” in Information the toolkit Source. framework and in other sections. 26 Local One source of Enables an Character Example 1: Alternative 1: Information Local alternative string. “AV3015” ID of other Source* Information source of Example 2: “EI01” local will normally other local Example 3: condition be the actual conditions to “OLCFile01.exe” provider from source of the be used to Other Local data. This facilitate Condition attribute testing and Table. allows that the simulation. Alternative 2: input data may Valid ID from be received among from Connection alternative Table records sources. Alternative 3: Valid filename in known directory.

A transactive node may possess many assets, and each asset may invoke multiple input information streams. Therefore, the local information connections should be carefully defined in the connection state model, and two attributes have been grouped as local information connection attributes.

A local information connection is an input that is invoked by and used by a toolkit function. Experimenters and testing personnel may wish to intentionally insert other alternative input information into the toolkit functions via this local information to simulate alternative scenarios that would be unlikely to occur under normal operations. Attribute 48 has been provided for this purpose, with which the source of the local information may be received from either the normal information provider or from an alternative source like an input file. Table 19 lists which of the state model's commands and events are expected to modify the two Other Local Condition Attributes.

TABLE 19 Ways in Which Local Information Connection Attributes May be Affected by Commands and Events in this State Model Loss or Re-Establish Terminate Configure Configuration Connect Disconnect Connection Connection Connection Attribute # Attribute Name Command Test** Command Command Event Event Event 48 Local Information ID* C + 0 C (C) (C) (C) (C) (C) 26 Local Information +0 C (C) (C) (C) (C) (C) Source* *These attributes should be configured before a local information connection can enter its 2 - Configured state. **The connection Configuration Test additionally should check that 32 - Connection Status has been configured.

6.1.14 Functions and Events of the Connection State Model

Configure( ) (Connection Attributes) Command—the same flexible command that was applied to the transactive node may also be used for configuring the connections that a transactive node manages. Only the new parameters that should be used for connections will be presented; most parameters that were used for the transactive node state model will not be repeated. This command is used with connection attributes by first referring to the respective connection identifier (e.g., contents of attributes 52, 2, 53, or 48) and setting or modifying that connection's remaining attributes.

-   -   Command Parameters         -   ConfigureFile=(Filename)—If a file is named using this             parameter, a command script will be read from Filename found             in a known file directory. It is recommended that the             Filename should contain scripted parameters as would be used             an in-line command.         -   Any combination of the following comma-separated, in-line             command parameters may be used and in any order:         -   TransactiveNeighbor=(52—Transactive Neighbor ID)—If the             Transactive Neighbor ID does not match an existing one,             configure a new Transactive Neighbor ID. The commands that             follow this command in the sequence of command parameters             are assumed to refer to this transactive neighbor             connection. This command parameter may be used again to             reference another transactive neighbor connection.             -   TransactiveNeighborDelete—Remove the record for the most                 recently referenced Transactive Neighbor ID.             -   TransactiveNeighborAttribute=attribute #, attribute                 value 1 [[, attribute value 2], . . . ]—This parameter                 may be used to initialize or change the contents of any                 transactive neighbor connection attribute except                 attributes 52—Transactive Neighbor ID and 32—Connection                 Status.         -   SystemManager=(53—System Manager ID)—If the System Manager             ID does not match an existing one, configure a new System             Manager ID. The commands that follow this command in the             sequence of command parameters are assumed to refer to this             system manager connection. This command parameter may be             used again to reference another system manager connection.             -   SystemManagerDelete—Remove the record for the most                 recently referenced System Manager ID.             -   SystemManagerAttribute=attribute #, attribute value 1                 [[, attribute value 2], . . . ]—This parameter may be                 used to initialize or change the contents of any system                 manager connection attribute except attributes 53—System                 Manager ID and 32—Connection Status.         -   Asset=(2—Asset ID)—If the Asset ID does not match an             existing one, configure a new Asset ID. the commands that             follow this command in the sequence of command parameters             are assumed to refer to this asset connection. This command             parameter may be used again to reference another asset             connection.             -   AssetDelete—Remove the record for the most recently                 referenced Asset ID.             -   AssetAttribute=attribute #, attribute value 1 [[,                 attribute value 2], . . . ]—This parameter may be used                 to initialize or change the contents of any asset                 connection attribute except attributes 2—Asset ID and                 32—Connection Status.         -   LocalInformation=(48—Local Information ID)—If the Local             Information ID does not match an existing one, configure a             new Local Information ID. The commands that follow this             command in the sequence of command parameters are assumed to             refer to this local information connection. This command             parameter may be used again to reference another local             information connection.             -   LocalInformationDelete—Remove the record for the most                 recently referenced Local Information ID.             -   LocalInformationAttribute=attribute #, attribute value                 1[[, attribute value 2], . . . ]—This parameter may be                 used to initialize or change the contents of any local                 information connection attribute except attributes                 48—Local Information ID and 32—Connection Status.     -   Command Logic         -   If the entity that made this command is not the local system             manager and if the entity has not been explicitly given             permission to make this system management command among the             commands in its 31—Connection Partner's System Management             Permissions, then reply             -   “Command failed—(F1) Permissions do not include this                 command”         -   From transactive node part of state model, which addressed             the Configure function, if attribute 7—Node Status=“5”             (state 5—Operational), then reply             -   “Command failed—(F2) Configure command not allowed from                 Operational state.”         -   If 32—Connection Status is “3” (connection state             3—Connected) or “4” (connection state 4—Lost Connection),             from which configuration of a connection is not be             permitted, then reply             -   “Command failed—(F12) Configure command not allowed from                 connected connection states.”         -   If Filename cannot be found, reply             -   “Command failed—(F3) File cannot be found or opened”         -   Failure conditions F4 (Incorrect node ID) and F5 (Command             did not address known node attributes) do not apply during             configuration of connections but should be reserved             nonetheless.         -   If the entity making this system management command attempts             to change a given connection's attributes, but the entity is             not listed among this connection's 30—Entities Permitted to             Modify this Connection (applies to any of the types of             connections), then reply             -   “Command failed—(F7) Entity making command does not have                 permission to configure this connection.”         -   If the transactive neighbor connection attribute number does             not match a known transactive neighbor connection attribute             number (e.g., is not a member of {10, 11, 12, 13, 17, 23,             24, 28, 29, 30, 31, 33}), or if no 52—Transactive Neighbor             ID has been stated as a parameter before this command             attempts to configure its attributes, then reply             -   “Command failed—(F8) Command did not address known                 transactive neighbor connection attributes”         -   If the system manager connection attribute number does not             match a known system manager connection attribute number             (e.g., is not a member of {17, 28, 29, 30, 31, and 33}), or             if no 53—System Manager ID has been stated as a parameter             before this command attempts to configure its attributes,             then reply             -   “Command failed—(F9) Command did not address known                 system manager connection attributes”         -   If the asset connection attribute number does not match a             known asset connection attribute number (e.g., is not a             member of {6, 17, 25, 28, 29, 30, 31, 33, 36, and 38}), or             if no 2—Asset ID has been stated as a parameter before this             command attempts to configure its attributes, then reply             -   “Command failed—(F10) Command did not address known                 asset connection attributes”         -   If the local information connection attribute number does             not match a known local information connection attribute             number (e.g., is not a member of {17, 26, 28, 29, 30, 31,             and 33}), or if no 48—Local Information ID has been stated             as a parameter before a command attempts to configure its             attributes, then reply             -   “Command failed—(F11) Command did not address known                 local information connection attributes”         -   If the command cannot be completed for any other reason,             reply             -   “Command failed—(F6) Unknown reason”         -   Otherwise,             -   Reply, “Command succeeded—(S1)”             -   Finalize any changes to connection attributes that were                 specified in the file or in-line command.             -   Run a Connection Configuration Test on this connection.             -   Run a transactive node Configuration Test on this                 transactive node.

Connection Configuration Test( )—a simple test of a given connection's attributes to determine if the connection may transition into or remain in its 2—Configured state. A connection in either its 3—Connected or 4—Lost Connection state has, by definition passed its Connection Configuration Test. If a connection passes its Connection Configuration Test, it should be in state 2—Configured; if it fails, it should be in state 1—Listed.

A Connection Configuration Test is not a system command. It should be initiated by the logic of the transactive node and by the transactive node itself. It should be run for a given connection anytime that the Configure( ) command has run successfully and might have therefore modified the configuration of the connection.

-   -   Test Parameters         -   All=test each connection according to its connection type         -   TransactiveNeighbor=(52—Transactive Neighbor ID)—conduct the             test on this transactive neighbor connection.         -   SystemManager=(53—System Manager ID)—conduct the test on             this system manager connection.         -   Asset=(2—Asset ID)—conduct the test on this asset             connection.         -   LocalInformation=(48—Local Information ID)—conduct the test             on this local information connection.     -   Test Logic         -   If upon checking attribute 32—Connection Status for a             connection, this connection is found to be in either state             “3” (3—Connected) or “4” (4—Lost Connection), then             -   Test passed—(S1) The Connected and Lost Connection                 states, by definition, pass the Connection Configuration                 Test         -   For each configured 52—Transactive Neighbor ID, if any of             the attributes 10—Receive TIS Source, 11—Receive TFS Source,             12—Send TIS Targets, 13—Send TFS Targets, 32—Connection             Status, or 29—Connection Partner Type have not been             configured, then             -   Test failed—(F1) Transactive neighbor connection is not                 configured             -   Set attribute 32—Connection Status=“1” (connection state                 1—Listed) for this connection.         -   For each configured 53—System Manager ID, if either of the             attributes 32—Connection Status or 29—Connection Partner             Type have not been configured, then             -   Test failed—(F2) System manager connection is not                 configured             -   Set attribute 32—Connection Status=“1” (connection state                 1—Listed) for this connection.         -   For each configured 2—Asset ID, if any of the attributes             6—Toolkit Function, 25—Asset Output Targets, 32—Connection             Status, or 29—Connection Partner Type have not been             configured, then             -   Test failed—(F3) Asset connection is not configured             -   Set 32—Connection Status=“1” (connection state 1—Listed)                 for this connection.         -   For each configured 48—Local Information ID, if any of the             attributes 26—Local Information Source, 32—Connection             Status, or 29—Connection Partner Type have not been             configured, then             -   Test failed—(F4) Local information connection is not                 configured             -   Set 32—Connection Status=“1” (connection state 1—Listed)                 for this connection.         -   Otherwise             -   Test passed—(S2)             -   Set 32—Connection Status=“2” (connection state                 2—Configured) for this connection.

Connect( ) Command—directs a configured connections to be completed between this transactive node and one of its connection partners.

-   -   Command Parameters         -   Connection=([All/Connection ID])—identifies one connection             that is to be completed from this transactive node to a             configured connection with a transactive neighbor, system             manager, asset, or local information source. If the             parameter “All” is used, the transactive node should attempt             to apply the command logic sequentially to every configured             connection (e.g., every connection for which a             52—Transactive Neighbor ID, 53—System Manger ID, 2—Asset ID,             or 48—Local Information ID has been configured).     -   Command Logic         -   If the entity that made this command is not the local system             manager and if the entity has not been explicitly given             permission to make this system management command among the             commands in its 31—Connection Partner's System Management             Permissions, then reply             -   “Command failed—(F1) Permissions do not include this                 command.”         -   If the Connection ID parameter of this command cannot be             recognized from among the sets of configured 52—Transactive             Neighbor ID, 52—System Manager ID, 2—Asset ID, or 48—Local             Information ID at this transactive node, then reply             -   “Command failed—(F2) Connection ID was not recognized                 from configured connections.”         -   If the entity making this command is not among the             30—Entities Permitted to modify this Connection for the             referenced connection, then reply             -   “Command failed—(F3) Entity does not have permission to                 change this connection.”         -   If upon review of its 32—Connection Status, the referenced             connection is determined to be in its 3—Connected state,             then reply             -   “Command succeeded—(S1) Connection was already                 completed.”         -   If upon review of its 32—Connection Status, the referenced             connection is determined to be in its 1—Listed state, then             reply             -   “Command failed—(F4) Connection cannot be completed from                 present connection state.”         -   If the given connection cannot be completed for any other             reason, reply             -   “Command failed—(F5) Unknown reason”             -   If 32—Connection Status=“3” (connection status                 3—Connected), then set 32—Connection Status=“2”                 (connection state 2—Configured) for the referenced                 connection.         -   Otherwise,             -   Reply, “Command succeeded—(S2)”             -   Complete the referenced connection             -   Set 32—Connection Status=“3” (connection state                 3—Connected) for the referenced connection.

Disconnect( ) Command—system management command by which a transactive node is asked to disconnect a connection between this transactive node and one of its connection partners.

-   -   Command Parameters         -   Connection=([All/Connection ID])—identifies one connection             that is to be disconnected between this transactive node and             a transactive neighbor, system manager, asset, or local             information source. If the parameter “All” is used, the             transactive node should attempt to apply the command logic             sequentially to every configured connection (e.g., every             connection for which a 52—Transactive Neighbor ID, 53—System             Manger ID, 2—Asset ID, or 48—Local Information ID has been             configured).     -   Command Logic         -   If the entity that made this command is not the local system             manager and if the entity has not been explicitly given             permission to make this system management command among the             commands in its 31—Connection Partner's System Management             Permissions, then reply             -   “Command failed—(F1) Permissions do not include this                 command.”         -   If the Connection ID parameter of this command cannot be             recognized from among the sets of configured 52—Transactive             Neighbor ID, 52—System Manager ID, 2—Asset ID, or 48—Local             Information ID at this transactive node, then reply             -   “Command failed—(F2) Connection ID was not recognized                 from configured connections.”         -   If the entity making this command is not among the             30—Entities Permitted to modify this Connection for the             referenced connection, then reply             -   “Command failed—(F3) Entity does not have permission to                 change this connection.”         -   If upon review of its 32—Connection Status, the referenced             connection is determined to be in either its 2—Configured or             1—Listed state, then reply             -   “Command succeeded—(S1) Connection was already                 disconnected.”         -   If the given connection cannot be completed for any other             reason, reply             -   “Command failed—(F4) Unknown reason”         -   Otherwise,             -   Reply, “Command succeeded—(S2)”             -   Disconnect the referenced connection             -   Set 32—Connection Status=“2” (connection state                 2—Configured) for the referenced connection.

Loss of Connection Event( )—a diagnostic process at this transactive node observes the health and activity of each connection. If the connection should fail, the diagnostic process initiates a Loss of Connection Event. This event transitions the respective connection into a temporary Lost Connection state, from which the ramifications of the event may be addressed and handled. This transactive node is permitted to remain in its Operational state in the meantime, according to the logic of the present state model.

-   -   Event Parameters—None.     -   Said “diagnostic process” should apply to a connection that is         in either its 3—Connected or 4—Lost Connection states. The means         by which a connection may be monitored may involve one or more         of these suggested mechanisms:         -   Observation of interactions with connection partners that             occur or fail to occur at times that such interactions were             anticipated         -   Occasional “pings” of connection partners to determine             whether they remain communicative         -   A “heartbeat” mechanism that ensures connection partners             that a connection remains active. (A “heartbeat” between             transactive neighbors should be bidirectional because both             transactive neighbors will share this to monitor the             connection. Other connection partners may not be transactive             nodes, in which case the heartbeat may be unidirectional to             satisfy the transactive node)     -   Event Handler Logic         -   This logic applies to a connection that is in its             3—Connected state.         -   If a connection is no longer working based on findings from             the diagnostic process,             -   Set 32—Connection Status=“4” (connection state 4—Lost                 Connection) for this connection.         -   Add a record of the UTC standard time at which the event             occurred into the 55—Lost Connection Event Buffer.         -   Start a timer to keep track of how long this connection             remains in its Lost Connection state.

Re-Establish Connection Event( )—a diagnostic process recognizes that a connection has become restored for a connection that was in its Lost Connection state. The connection reverts to its Connected state.

-   -   Event Parameters—None.     -   This event handler should use the same diagnostic process that         was described above for the Loss of Connection Event.     -   Event Handler Logic         -   This logic applies only to a connection that is in its             temporary Lost Connection state.         -   If prior to the occurrence of a Terminate Connection Event(             ), this transactive node recognizes that a lost connection             has become restored, then             -   Set 32—Connection Status=“3” (connection state                 3—Connected) for the respective connection             -   Stop the Loss of Connection Event timer.             -   Re-commence interactions with the respective connection                 partner via this connection.

Terminate Connection Event( )

-   -   Event Parameters—None.     -   This event handler should use the same diagnostic process that         was described above for the Loss of Connection Event and         Re-Establish Connection Event.     -   Event Handler Logic         -   This logic applies to a connection that is in its Lost             Connection state.         -   If the Loss of Connection Event timer exceeds 54—Connection             Timeout Period for this connection, then             -   Set 32—Connection Status=“2” (connection state                 2—Configured) for this connection             -   Issue alert, “(A1)—Terminate Connection Event occurred                 by timeout for connection [Connection ID].”         -   If upon reviewing the contents of the 55—Loss of Connection             Event Buffer it is observed that the numbers of Loss of             Connection Events in the last hour has exceeded the criteria             in 56—Allowed Frequency of Loss of Connection Events, then             -   Set 32—Connection Status=“2” (connection state                 2—Configured) for this connection             -   Issue alert, “(A2)—Terminate Connection Event—Too many                 hourly events for connection [Connection ID].”         -   If upon reviewing the contents of the 55—Loss of Connection             Event Buffer the numbers of Loss of Connection Events in the             last 24 hours has exceeded the criteria in 56—Allowed             Frequency of Loss of Connection Events, then             -   Set 32—Connection Status=“2” (connection state                 2—Configured) for this connection             -   Issue alert, “(A3)—Terminate Connection Event—Too many                 daily events for connection [Connection ID].”

6.1.15 Connection State Transition Table

Table 20 is the state transition table for the four types of connections that are to be managed by a transactive node. Refer to the diagrammatic representation of the connection state transitions in FIG. 30 that should represent these same state transitions.

TABLE 20 State Transition Table for Connections of Four Types Acts Upon Producing Info. Internal Current To Set Next On the Gathered & Row Function State Using Input Attributes State Output Condition Recorded 11a Connection 1 - Listed Connection 1 - Listed Connection Test failed - Connection Configuration attributes 2 - event log [(F1) Transactive event log Test Asset ID, entry neighbor entry Failed 10 - connection Receive is not TIS configured/ Source, 11 - (F2) System Receive manager TFS connection Source, 12 - is not Sent TIS configured/ Targets, 13 - (F3) Asset Send connection TFS is not Targets, 25 - configured/ Asset (F4) Local Output information Targets, 26 - connection Local is not Information configured] Source, 32 - Connection Status, 29 - Connection Partner Type, 48 - Local Information ID, 52 - Transactive Neighbor ID, and 53 - System Manager ID 11b Configure 1 - Listed Source of Nearly 1 - Listed Reply: Success - Connection command; any “Command (S1) command command connection Succeeded - log entry parameters; attribute (S1)” Filename; may be Action: Run lists of configured. Connection configurable See Configuration attributes the Test (see command Action: Run command definition Configuration definition), for Test connection details. Connection attributes Lists of command 2 - Asset configurable log entry ID, 30 - attributes Entities may be Permitted found in to Modify the this command Connection, definition. 31 - Connection Partner's System Management Permissions, 32 - Connection Status, 48 - Local Information ID, 52 - Transactive Neighbor ID, 53 - System Manager ID 11c Disconnect 1 - Listed Source of 1 - Listed Reply: Success - Connection command; “Command (S1) command command succeeded - Connection log entry parameters; (S1) already connection Connection disconnected attributes already 2 - Asset disconnected” ID, Connection 30 - Entities command Permitted log entry to Modify this Connection, 31 - Connection Partner's System Management Permissions, 32 - Connection Status, 48 - Local Information ID, 52 - Transactive Neighbor ID, 53 - System Manager ID 11d Fail to 1 - Listed Source of 1 - Listed Reply: Command Connection Configure command; “Command failed - command command failed - [(F1) Permissions log entry parameters; [(F1) Permissions do Filename; do not include lists of not include this configurable this command/ attributes command/ (F2) (see (F2) Configure command Configure command definition), command not allowed connection not allowed from attributes from Operational 2 - Asset Operational state/ ID, 30 - state/ (F3) File Entities (F3) File cannot be Permitted cannot be found or to Modify found or opened/ this opened/ (F7) Entity Connection, (F7) Entity making 31 - Connection making command Partner's command does not System does not have Management have permission Permissions, permission to configure 32 - Connection to configure this Status, this connection/ 48 - Local connection/ (F8) Command Information (F8) Command did not ID, did not address 52 - Transactive address known Neighbor known transactive ID, transactive neighbor 53 - System neighbor connection Manager connection attributes/ ID attributes/ (F9) (F9) Command Command did not did not address address known known system system manager manager connection connection attributes/ attributes/ (F10) Command (F10) Command did did not address not address known known asset asset connection connection attributes/ attributes/ (F11) Command (F11) Command did did not address not address known local known local information information connection connection attributes/ attributes/ (F6) (F6) Unknown Unknown reason] reason]” Connection command log entry 11e Fail to 1 - Listed Source of 1 - Listed Reply: Failure - Connection Connect command; “Command [(F1) Permissions command command failed - do log entry parameters; [(F1) Permissions not include connection do this attributes not include command/ 2 - Asset this (F2) Connection ID, command/ ID 30 - Entities (F2) Connection was not Permitted ID recognized to Modify was not from this recognized configured Connection, from connections/ 31 - Connection configured (F3) Entity Partner's connections/ does not System (F3) Entity have Management does not permission Permissions, have to change 32 - Connection permission this Status, to change connection/ 48 - Local this (F4) Connection Information connection/ cannot be ID, (F4) Connection completed 52 - Transactive cannot be from Neighbor completed present ID, from connection 53 - System present state] Manager connection ID state]” Connection command log entry 11f Fail to 1 - Listed Source of 1 - Listed Reply: Failure - Connection Disconnect command; “Command [(F1) Permissions command command failed - do log entry parameters; [(F1) Permissions not include connection do this attributes not include command/ 2 - Asset this (F2) Connection ID, command/ ID 30 - Entities (F2) Connection was not Permitted ID recognized to Modify was not from this recognized configured Connection, from connections/ 31 - Connection configured (F3) Entity Partner's connections/ does not System (F3) Entity have Management does not permission Permissions, have to change 32 - Connection permission this Status, to change connection/ 48 - Local this (F4) Unknown Information connection/ reason] ID, (F4) Unknown 52 - Transactive reason]” Neighbor Connection ID, command 53 - System log entry Manager ID 12 Connection 1 - Listed Connection 32 - 2 - Configured Connection Test Connection Configuration attributes 2 - Connection event log passed - event log Test Asset ID, Status = entry (S2) entry Passed 10 - “2” Normal Receive (connection pass TIS state condition Source, 11 - 2 - Configured) Receive TFS Source, 12 - Sent TIS Targets, 13 - Send TFS Targets, 25 - Asset Output Targets, 26 - Local Information Source, 32 - Connection Status, 29 - Connection Partner Type, 48 - Local Information ID, 52 - Transactive Neighbor ID, and 53 - System Manager ID 21 Connection 2 - Configured Connection 1 - Listed Connection Test failed - Connection Configuration attributes 2 - event log [(F1) Transactive event log Test Asset ID, entry neighbor entry Failed 10 - connection Receive is not TIS configured/ Source, 11 - (F2) System Receive manager TFS connection Source, 12 - is not Sent TIS configured/ Targets, 13 - (F3) Asset Send connection TFS is not Targets, 25 - configured/ Asset (F4) Local Output information Targets, 26 - connection Local is not Information configured] Source, 32 - Connection Status, 29 - Connection Partner Type, 48 - Local Information ID, 52 - Transactive Neighbor ID, and 53 - System Manager ID 22a Configure 2 - Configured Source of Nearly 2 - Configured Reply: Success - Connection command; any “Command (S1) command command connection Succeeded - log entry parameters; attribute (S1)” Filename; may be Action: Run lists of configured. Connection configurable See Configuration attributes the Test (see command Action: Run command definition Configuration definition), for Test connection details. Connection attributes Lists of command 2 - Asset configurable log entry ID, 30 - attributes Entities may be Permitted found in to Modify the this command Connection, definition. 31 - Connection Partner's System Management Permissions, 32 - Connection Status, 48 - Local Information ID, 52 - Transactive Neighbor ID, 53 - System Manager ID 22b Connection 2 - Configured Connection 2 - Configured Connection Test Connection Configuration attributes 2 - event log passed - event log Test Asset ID, entry (S2) entry Passed 10 - Normal Receive pass TIS condition Source, 11 - Receive TFS Source, 12 - Sent TIS Targets, 13 - Send TFS Targets, 25 - Asset Output Targets, 26 - Local Information Source, 32 - Connection Status, 29 - Connection Partner Type, 48 - Local Information ID, 52 - Transactive Neighbor ID, and 53 - System Manager ID 22c Disconnect 2 - Configured Source of 2 - Configured Reply: Success - Connection command; “Command (S1) command command succeeded - Connection log entry parameters; (S1) already connection Connection disconnected attributes already 2 - Asset disconnected” ID, Connection 30 - Entities command Permitted log entry to Modify this Connection, 31 - Connection Partner's System Management Permissions, 32 - Connection Status, 48 - Local Information ID, 52 - Transactive Neighbor ID, 53 - System Manager ID 22d Fail to 2 - Configured Source of 2 - Configured Reply: Command Connection Configure command; “Command failed - command command failed - [(F1) Permissions log entry parameters; [(F1) Permissions do Filename; do not include lists of not include this configurable this command/ attributes command/ (F2) (see (F2) Configure command Configure command definition), command not allowed connection not allowed from attributes from Operational 2 - Asset Operational state/ ID, 30 - state/ (F3) File Entities (F3) File cannot be Permitted cannot be found or to Modify found or opened/ this opened/ (F7) Entity Connection, (F7) Entity making 31 - Connection making command Partner's command does not System does not have Management have permission Permissions, permission to configure 32 - Connection to configure this Status, this connection/ 48 - Local connection/ (F8) Command Information (F8) Command did not ID, did not address 52 - Transactive address known Neighbor known transactive ID, transactive neighbor 53 - System neighbor connection Manager connection attributes/ ID attributes/ (F9) (F9) Command Command did not did not address address known known system system manager manager connection connection attributes/ attributes/ (F10) Command (F10) Command did did not address not address known known asset asset connection connection attributes/ attributes/ (F11) Command (F11) Command did did not address not address known local known local information information connection connection attributes/ attributes/ (F6) (F6) Unknown Unknown reason] reason]” Connection command log entry 22e Fail to 2 - Configured Source of 2 - Configured Reply: Failure - Connection Connect command; “Command [(F1) Permissions command command failed - do log entry parameters; [(F1) Permissions not include connection do this attributes not include command/ 2 - Asset this (F2) Connection ID, command/ ID 30 - Entities (F2) Connection was not Permitted ID recognized to Modify was not from this recognized configured Connection, from connections/ 31 - Connection configured (F3) Entity Partner's connections/ does not System (F3) Entity have Management does not permission Permissions, have to change 32 - Connection permission this Status, to change connection/ 48 - Local this (F5) Unknown Information connection/ reason] ID, (F5) Unknown 52 - Transactive reason]” Neighbor Connection ID, command 53 - System log entry Manager ID 22f Fail to 2 - Configured Source of 2 - Configured Reply: Failure - Connection Disconnect command; “Command [(F1) Permissions command command failed - do log entry parameters; [(F1) Permissions not include connection do this attributes not include command/ 2 - Asset this (F2) Connection ID, command/ ID 30 - Entities (F2) Connection was not Permitted ID recognized to Modify was not from this recognized configured Connection, from connections/ 31 - Connection configured (F3) Entity Partner's connections/ does not System (F3) Entity have Management does not permission Permissions, have to change 32 - Connection permission this Status, to change connection/ 48 - Local this (F4) Unknown Information connection/ reason] ID, (F4) Unknown 52 - Transactive reason]” Neighbor Connection ID, command 53 - System log entry Manager ID 23 Connect 2 - Configured Source of 32 - 3 - Connected Reply: Command Connection command; Connection “Command succeeded - command command Status = succeeded - (S2) log entry parameters; “3” (S2)” Normal connection (connection Connection completion attributes state command 2 - Asset 3 - log entry ID, Connected) 30 - Entities Permitted to Modify this Connection, 31 - Connection Partner's System Management Permissions, 32 - Connection Status, 48 - Local Information ID, 52 - Transactive Neighbor ID, 53 - System Manager ID 32 Disconnect 3 - Connected Source of 32 - 2 - Configured Reply: Success - Connection command; Connection “Command (S2) command command Status = succeeded - Normal log entry parameters; “2” (S2)” completion connection (connection Action: attributes state Sever 2 - Asset 2 - Configured) connection ID, to this 30 - Entities communication Permitted partner to Modify Connection this command Connection, log entry 31 - Connection Partner's System Management Permissions, 32 - Connection Status, 48 - Local Information ID, 52 - Transactive Neighbor ID, 53 - System Manager ID 33a Connection 3 - Connected Connection 3 - Connected Connection Test Connection Configuration attributes 2 - event log passed - event log Test Asset ID, entry (S1) entry Passed 10 - Connection Receive already TIS completed Source, 11 - Receive TFS Source, 12 - Sent TIS Targets, 13 - Send TFS Targets, 25 - Asset Output Targets, 26 - Local Information Source, 32 - Connection Status, 29 - Connection Partner Type, 48 - Local Information ID, 52 - Transactive Neighbor ID, and 53 - System Manager ID 33b Connect 3 - Connected Source of 3 - Connected Reply: Command Connection command; “Command succeeded - command command succeeded - (S1) log entry parameters; (S1) Connection connection Connection already attributes already made 2 - Asset made” ID, Connection 30 - Entities command Permitted log entry to Modify this Connection, 31 - Connection Partner's System Management Permissions, 32 - Connection Status, 48 - Local Information ID, 52 - Transactive Neighbor ID, 53 - System Manager ID 33c Fail to 3 - Connected Source of 3 - Connected Reply: Command Connection Configure command; “Command failed - command command failed - [(F1) Permissions log entry parameters; [(F1) Permissions do Filename; do not include lists of not include this configurable this command/ attributes command/ (F2) (see (F2) Configure command Configure command definition), command not allowed connection not allowed from attributes from Operational 2 - Asset Operational state/ ID, 30 - state/ (F12) Configure Entities (F12) Configure command Permitted command not allowed to Modify not allowed from this from connected Connection, connected connection 31 - Connection connection states] Partner's states]” System Connection Management command Permissions, log entry 32 - Connection Status, 48 - Local Information ID, 52 - Transactive Neighbor ID, 53 - System Manager ID 33d Fail to 3 - Connected Source of 3 - Connected Reply: Failure - Connection Connect command; “Command [(F1) Permissions command command failed - do log entry parameters; [(F1) Permissions not include connection do this attributes not include command/ 2 - Asset this (F2) Connection ID, command/ ID 30 - Entities (F2) Connection was not Permitted ID recognized to Modify was not from this recognized configured Connection, from connections/ 31 - Connection configured (F3) Entity Partner's connections/ does not System (F3) Entity have Management does not permission Permissions, have to change 32 - Connection permission this Status, to change connection/ 48 - Local this (F5) Unknown Information connection/ reason] ID, (F5) Unknown 52 - Transactive reason]” Neighbor Connection ID, command 53 - System log entry Manager ID 33e Fail to 3 - Connected Source of 3 - Connected Reply: Failure - Connection Disconnect command; “Command [(F1) Permissions command command failed - do log entry parameters; [(F1) Permissions not include connection do this attributes not include command/ 2 - Asset this (F2) Connection ID, command/ ID 30 - Entities (F2) Connection was not Permitted ID recognized to Modify was not from this recognized configured Connection, from connections/ 31 - Connection configured (F3) Entity Partner's connections/ does not System (F3) Entity have Management does not permission Permissions, have to change 32 - Connection permission this Status, to change connection/ 48 - Local this (F4) Unknown Information connection/ reason] ID, (F4) Unknown 52 - Transactive reason]” Neighbor Connection ID, command 53 - System log entry Manager ID 34 Loss of 3 - Connected Diagnostic 32 - 4 - Lost Connection Diagnostic Connection Connection system Connection Connection event log system event log Event information Status = entry detects that a entry from the “4” connection system that (connection to a oversees state connection connections; 4 - Lost partner is identity Connection), dead while of affected and that connection; 55 - connection and Loss of is in its connection Connection Connected attributes Event state 18 - Time, Buffer 32 - Connection Status 42a Terminate 4 - Lost Diagnostic 32 - 2 - Configured “Alert - [(A1) Terminate Connection Connection Connection system Connection [(A1) Terminate Connection event log Event information Status = Connection Event entry from the “2” Event occurred by system that (connection occurred by timeout for oversees state timeout for connection connections; 2 - Configured) connection [Connection identity [Connection ID]/ of affected ID]/ (A2) Terminate connection; (A2) Terminate Connection and Connection Event - connection Event - Too many attributes Too many hourly 18 - Time, hourly events for 32 - Connection events for connection Status, connection [Connection 54 - Connection [Connection ID]/ Timeout ID]/ (A3) Terminate Period, (A3) Terminate Connection 55 - Loss Connection Event - of Event - Too many Connection Too many daily Event daily events for Buffer, events for connection 56 - Allowed connection [Connection Frequency [Connection ID]] of Loss of ID]]” Connection Connection Events event log entry 42b Disconnect 4 - Lost Source of 32 - 2 - Configured Reply: Success - Connection Connection command; Connection “Command (S2) command command Status = succeeded - Normal log entry parameters; “2” (S2)” completion connection (connection Action: attributes state Sever 2 - Asset 2 - Configured) connection ID, to this 30 - Entities communication Permitted partner to Modify Connection this command Connection, log entry 31 - Connection Partner's System Management Permissions, 32 - Connection Status, 48 - Local Information ID, 52 - Transactive Neighbor ID, 53 - System Manager ID 43a Connect 4 - Lost Source of 32 - 3 - Connected Reply: Command Connection Connection command; Connection “Command succeeded - command command Status = succeeded - (S2) log entry parameters; “3” (S2)” Normal connection (connection Connection completion attributes state command 2 - Asset 3 - log entry ID, Connected) 30 - Entities Permitted to Modify this Connection, 31 - Connection Partner's System Management Permissions, 32 - Connection Status, 48 - Local Information ID, 52 - Transactive Neighbor ID, 53 - System Manager ID 43b Re- 4 - Lost Diagnostic 32 - 3 - Connected Action: Re- Diagnostic Connection Establish Connection system Connection establish system event log Connection information Status = interface to detects that entry Event from the “3” respective a broken system that (connection connection connection oversees state partner. to a connections; 3 - Connection connection identity Connected) event log partner has of affected entry become re- connection; established and while that connection connection attributes is in its Lost 18 - Time, Connection 32 - Connection state Status, and 54 - Connection Timeout Period 44a Connection 4 - Lost Connection 4 - Lost Connection Test Connection Configuration Connection attributes 2 - Connection event log passed - event log Test Asset ID, entry (S1) entry Passed 10 - Connection Receive already TIS completed Source, 11 - Receive TFS Source, 12 - Sent TIS Targets, 13 - Send TFS Targets, 25 - Asset Output Targets, 26 - Local Information Source, 32 - Connection Status, 29 - Connection Partner Type, 48 - Local Information ID, 52 - Transactive Neighbor ID, and 53 - System Manager ID 44b Fail to 4 - Lost Source of 4 - Lost Reply: Command Connection Configure Connection command; Connection “Command failed - command command failed - [(F1) Permissions log entry parameters; [(F1) Permissions do Filename; do not include lists of not include this configurable this command/ attributes command/ (F2) (see (F2) Configure command Configure command definition), command not allowed connection not allowed from attributes from Operational 2 - Asset Operational state/ ID, 30 - state/ (F12) Configure Entities (F12) Configure command Permitted command not allowed to Modify not allowed from this from connected Connection, connected connection 31 - Connection connection states] Partner's states]” System Connection Management command Permissions, log entry 32 - Connection Status, 48 - Local Information ID, 52 - Transactive Neighbor ID, 53 - System Manager ID 44c Fail to 4 - Lost Source of 4 - Lost Reply: Failure - Connection Connect Connection command; Connection “Command [(F1) Permissions command command failed - do log entry parameters; [(F1) Permissions not include connection do this attributes not include command/ 2 - Asset this (F2) Connection ID, command/ ID 30 - Entities (F2) Connection was not Permitted ID recognized to Modify was not from this recognized configured Connection, from connections/ 31 - Connection configured (F3) Entity Partner's connections/ does not System (F3) Entity have Management does not permission Permissions, have to change 32 - Connection permission this Status, to change connection/ 48 - Local this (F5) Unknown Information connection/ reason] ID, (F5) Unknown 52 - Transactive reason]” Neighbor Connection ID, command 53 - System log entry Manager ID 44d Fail to 4 - Lost Source of 4 - Lost Reply: Failure - Connection Disconnect Connection command; Connection “Command [(F1) Permissions command command failed - do log entry parameters; [(F1) Permissions not include connection do this attributes not include command/ 2 - Asset this (F2) Connection ID, command/ ID 30 - Entities (F2) Connection was not Permitted ID recognized to Modify was not from this recognized configured Connection, from connections/ 31 - Connection configured (F3) Entity Partner's connections/ does not System (F3) Entity have Management does not permission Permissions, have to change 32 - Connection permission this Status, to change connection/ 48 - Local this (F4) Unknown Information connection/ reason] ID, (F4) Unknown 52 - Transactive reason]” Neighbor Connection ID, command 53 - System log entry Manager ID

6.1.16 Log Entries

The state transition tables in this section have consistently indicated outputs to a log table. It will be good practice to create a log entry record for each command and event that is encountered by the transactive node and its connections. Instead of defining each log entry at the point that it was introduced in the state transition tables, it may be preferred to establish practices for the contents of these records based on their types and by whether they affect the transactive state model or that of the transactive node's connections:

-   -   1. Command log entry—to be recorded each time a transactive node         system management command is received and responded.         -   {Source of the command, time received, command ID, command             parameters, 5—Node Version, 7—Node Status after the command,             completion condition}     -   2. Connection command log entry—to be recorded each time a         connection system management command is received and responded.         -   {Source of the command, time received, command ID, target             connection ID, 32—Connection Status after the command,             completion condition}     -   3. Event log entry—to be recorded each time a transactive node         event occurs and is responded to.         -   {Event time, event ID, 5—Node Version, 7—Node Status after             the event, completion condition}     -   4. Connection event log entry—to be recorded each time a         connection event occurs and is responded to.         -   {Event time, event ID, target connection ID, 32—Connection             Status after the event, completion condition}     -   5. Test log entry—to be recorded each time a transactive node         test occurs and is responded to.         -   {Test time, test ID, 5—Node Version, 7—Node Status after the             test, completion condition}     -   6. Connection test log entry—to be recorded each time a         connection test occurs and is responded to.         -   {Test time, test ID, target connection ID, 32—Connection             Status after the test, completion condition

6.1.17 Operational Sub-States Table

The table below represents that state transitions of a transactive node that has been configured, connected and is now in the overall operational state and status. Note that there is no start or final state in this table. All states may be intermediary. Refer to the toolkit framework for the algorithmic framework facilitated by this part of the state model.

TABLE 21 State Transition Model for Transactive Nodes within an Operational State Acts Info. Upon By Producing Gathered Internal Current Setting Using Next On the and Row Function State Attributes Inputs State Output Condition Recorded A1 Receive Operational 7 TIS TIS U 1, 7. 18, TIS (Listening) Message Received Received TIS message A2 Formula Operational 7 Attribute TIS Stop TIS 1, 7. 18, te TIS (Listening) 23 (TIS Processed TIS Timer > Processed Buffer) Timer, TIS TIS Outgoing Timer message TIS Max or Message(s) TIS received from all inputs A3 Receive Operational 7 TFS TFS U 1, 7, 18 TFS (Listening) Message Received A4 TFS Operational 7 Attribute TFS Stop TFS (1), (7), (Listening) 24 (TFS Processed TFS Timer > (18), Buffer) Timer, TFS Processed Outgoing Timer TFS TFS Max or message Messages TFS received from all inputs. A5 Update TIS 7, 23 TIS Operational Start No TIS (1), (7), TIS Received Message (Listening) TIS Receive (18), 23 Buffer Timer Error, and Start TIS Timer if it is not already running. A6 Handle TIS 7 TIS Operational Non- TIS (1), (7), Non- Received Message (Listening) fatal TIS Receive (18) fatal TIS Receive Error Receive Error Error A6a Handle TIS 7 TIS Stopped Fatal Fatal TIS (1), (7), Fatal Received Message TIS Receive (18) TIS Receive Error Receive Error Error A7 Send TIS 7 Outgoing Operational TIS Send TIS (1), (7), TIS Processed TIS (Listening) Message(s) if and (18), Sent Messages to each only if a TIS neighbor TIS has messages not already been sent within the Time Interval A7a Send Operational 7 Outgoing Operational TIS Send TIS (1), (7), TIS (Listening) TIS (Listening) Receive if and (18), Sent Messages Error, only if TIS TIS any messages Message(s) inputs to each from neighbor neighbors have not been received within the time interval A7b Handle TIS 7 TIS TIS Recovery Non-fatal (1), (7), non- Processed Processing Processed TIS (18) fatal TIS Error Processing Received processing error TIS error Message, Generated error A7c Handle TIS 7 TIS Stopped Fatal TIS (1), (7), fatal TIS Processed Processing Processing (18) processing Error Error Received error TIS Message, Generated error A8 Update TFS 7, 24 TFS Operational Start No TFS (1), (7), TFS Received Message (Listening) TFS Receive (18), 24 Buffer Timer Error, and Start TFS timer if it is not already running A9 Handle TFS 7 TFS Operational Non- TFS (1), (7), Non- Received Message (Listening) fatal Receive (18) fatal TFS Error TFS Receive Receive Error Error A9a Handle TFS 7 TFS Stopped Fatal Fatal (1), (7), Fatal Received Message TFS TFS (18) TFS Receive Receive Receive Error Error Error A10 Send TFS 7 Outgoing Operational TFS Send (1), (7), TFS Processed TFS (Listening) Message(s) TFS if (18), Sent Messages to each and only TFS neighbor if a TFS messages has not already been sent within the time interval. A10a Send Operational 7 Outgoing Operational TFS Send (1), (7), TFS (Listening) TFS (Listening) Receive TFS if (18), Sent Messages Error, and only TFS TFS if any messages Message(s) inputs to each from our neighbor neighbors have not been received within the time interval. A11 Handle TFS 7 TFS TFS Recovery Non-fatal (1), (7), non- Processed Processing Processed TFS (18) fatal Error Processing Received TFS error TFS processing Message, error Generated error A11a Handle TFS 7 TFS Stopped Fatal (1), (7), fatal Processed Processing TFS (18) TFS Error Processing Received processing Error TFS error Message, Generated error (“U” = unconditional)

6.1.18 Transactive Control Signal Propagation 6.1.18.1 Problem Statement

Transactive control signals (transactive incentive signal and transactive feedback signal) carry information related to electrical power supply and demand over a wide area network. The signals traverse a network of transactive control nodes to elicit a desired control action from responsive assets in a timely manner. The end-to-end (from generation to end-user customer) transmission time should be less than 3 minutes assuming a transactive control hierarchy of 15 levels spanning a 1000 mile radius. This translates to roughly 12 seconds (180/15) per hop time budget including the link transit time. Note that the transactive incentive signals will start at the bulk generators and continue to end-user customers. The transactive feedback signal will start at the end-use customer and will travel through the transactive control hierarchy towards bulk generation. While the TIS and the TFS are decoupled temporally and loosely coupled functionally in the sense that a TFS generation does not have to get triggered by the arrival of a TIS, the two signals still influence each other since the computation of TIS and TFS considers the forecasted values for each signal.

The timing model can be purely clock-driven or more asynchronously event-driven. For example, in some embodiments, a set of neighboring transactive nodes are configured to exchange transactive values with one another until the transactive values converge with one another to an acceptable degree (e.g., within a designated percentage of one another (such as 5%, 2%, 1%, or any other desired degree of tolerance)). Further, in such even-driven systems, when a change occurs within a transactive node (e.g., due to a change in local conditions), the transactive node can be configured to transmit an updated set of transactive signals when its local transactive signals deviate from the previously transmitted signals by more than a relaxation criterion.

If the system becomes highly synchronized, bursts of signals might tax the system infrastructure. If the system becomes too loosely event-driven and asynchronous, it becomes more difficult to confirm that signals will have been conveyed. There is probably some flexibility allowable between these extremes, and the design in this appendix facilitates some flexibility. Regardless, the timing model should recognize that the “conversation” of these signals necessarily changes during the transition from one update interval to the next because the set of future intervals change during this transition.

FIG. 31 is a diagram 3100 showing TIS and TFS generation being decoupled. The processing of TIS and TFS inputs is performed in reference to the basic 5-minute interval structure that is UTC referenced.

6.1.18.2 Transactive Node Object Model Attributes Summary

A set of ten (and in some embodiments, mandatory), configurable attributes B1-B10 are defined below in Table 22.

6.1.18.3 One Exemplary Approach

-   -   1. Transactive control nodes of the Demonstration use time         synchronization with a tolerance of 200 ms. This is readily         achievable using either NTP or SNTP. The synchronization is         useful to align transactive signal intervals as well as ease of         correlation of data collection and event logs.     -   2. Each transactive control node has two transactive signal         timers: TIS_TIMER and TFS_TIMER. These timers are started upon         receipt of a TIS or TFS respectively and impose a delay to allow         for arrival of more signals before processing occurs (12 second         default value).     -   3. Each transactive control node has two “hold-down” timers:         TIS_HOLD_DOWN_TIMER and TFS_HOLD_DOWN_TIMER. These timers lock         out additional processing to prevent race conditions in the mesh         segment of a network of transactive control nodes. (30 second         default value). The value should be >=TIS_TIMER and TFS_TIMER         respectively.     -   4. Each transactive control node has a transactive signal         watchdog timer (WATCHDOG_TIMER), which is configured to fire off         every T_period (300 default value) seconds. It is desirable that         the WATCHDOG_TIMER be less than or equal to the value of the         smallest interval (currently 300 seconds) used in the         communication of the transactive signals.     -   5. Upon startup, a transactive control node starts the         transactive signal watchdog timer. It is recommended that the         watchdog timer be aligned with the transactive signal update         intervals. For example, if the transactive signal intervals are         {6:00, 6:05, 6:10, . . . } then the watchdog timer is         recommended to also be started at 6:00 and fire-off every 300         seconds.     -   6. When the transactive signal watchdog timer expires, if         WATCHDOG_TIMER_SIGNAL_GEN_ALWAYS_ON configuration variable is         set to TRUE then the node will send TIS and TFS packets to         neighboring transactive control nodes. If         WATCHDOG_TIMER_SIGNAL_GEN_ALWAYS_ON is set to FALSE and if no         signal driven events have taken place in the last interval then         the node sends TIS and TFS packets to neighboring transactive         control nodes connected to this node. Then, the node restarts         the global timer.     -   7. When the node receives a TIS packet, it starts the TIS_TIMER         (if it is not already started), and stores the TIS packet in the         local TIS store. The TIS_TIMER represents a transactive signal         collection period to allow the transactive control node to         receive all possible signals from its neighbors. (Each         transactive node typically knows how many transactive neighbors         it has and therefore how many transactive signals it should         expect to receive. In deeper topologies, the TIS_TIMER and         TFS_TIMER will unlikely achieve the desired effect of collecting         all signals prior to calculation because signal path lengths         will be dissimilar for various signals that are to be received.)     -   8. When the node receives a TFS packet, it starts the TFS_TIMER         (if it is not already started), stores the TFS packet in the         local TFS store. The TFS_TIMER represents a transactive signal         collection period to allow the transactive control node to         receive all possible signals from its neighbors.     -   9. When the TIS_TIMER expires, the node performs the transactive         control computation using the most recent TIS and TFS         information stored in its TIS and TFS stores. (Received TIS and         TFS signals will often contribute only a small influence to the         newly calculated TIS and TFS at a transactive node.)     -   10. When TFS_TIMER expires, the node starts performs the         transactive control computation using the most recent TIS and         TFS information stored in its TIS and TFS stores.     -   11. When the node finishes TIS signal computation, it clears the         store and sends a TIS packet to its neighbors (In simulations,         the processing is represented with a delay of 12 seconds). The         TIS_HOLD_DOWN_TIMER is started. No TIS may be sent again until         it expires.     -   12. When the node finishes the TFS signal computation, it clears         the cache and sends a TFS packet to its neighbors (In         simulations, the processing is represented with a delay of 12         seconds). The TFS_HOLD_DOWN_TIMER is started. No TFS may be sent         again until it expires.     -   13. Since the transactive control is a distributed system, there         will be times when transactive control signals arrive during the         hold-down timer or outside the TIS/TFS timer data collection         periods. TIS and TFS signals also may arrive at different parts         of the time interval. When a new transactive control signal is         received and the corresponding transactive control signal         computation is performed, one may find that the resulting         TIS/TFS values show no significant changes to the previously         sent values in the same “interval.” In this case, the         transactive control node is recommended to omit or delay the         transmission of a new TIS/TFS value. This added feature allows         further reductions of both communications chatter and         computational cycles. This behavior is controlled by means of         two configuration variables: TIS_SIGNAL_SUPPRESS_IF_NO_CHANGE         and TFS_SIGNAL_SUPPRESS_IF_NO_CHANGE. If either one of these         variables are set to TRUE, then the node will be perform the         check for no change of the corresponding TIS or TFS signals and         suppress transmission.     -   14. One of the primary inputs to the transactive control node is         the local conditions input. This section encourages inclusion of         triggers for computation and transmission of TIS/TFS based on         changes in the local conditions. The criteria for incorporation         of local conditions will be decided at a later time.

The timers and the operation for an example TIS embodiment are illustrated in diagram 3200 of FIG. 32. The TFS is handled in a similar manner.

In summary, the following desired behavior is expressed in pseudo code format.

Upon node startup:

-   -   Start WATCHDOG_TIMER

Upon receiving a TIS:

-   -   if (TIS_TIMER is not running) && (TIS_HOLD_DOWN_TIMER is not         running) && (!TIS_IN_CALCULATION) {Start TIS_TIMER}     -   Store received TIS

Upon receiving a TFS:

-   -   if (TFS_TIMER is not running) && (TFS_HOLD_DOWN_TIMER is not         running) && (!TIS_IN_CALCULATION) {Start TFS_TIMER}     -   Store received TFS

Upon expiration of TIS_TIMER:

-   -   Stop and clear TIS_TIMER     -   Set TIS_IN_CALCULATION==true)     -   Compute TIS using most recent stored TIS and TFS.     -   If (TIS_SIGNAL_SUPPRESS_IF_NO_CHANGE==FALSE) {Send TIS} else         {check for change in values of computed TIS with the previously         sent TIS. If change {Send TIS} else {do nothing}     -   Set TIS_IN_CALCULATION==false)     -   If (TIS is sent) {Start TIS_HOLD_DOWN_TIMER}

Upon expiration of TFS_TIMER:

-   -   Stop and clear TFS_TIMER     -   Set TFS IN_CALCULATION==true)     -   Compute TFS using most recent stored TIS and TFS.     -   If (TFS_SIGNAL_SUPPRESS_IF_NO_CHANGE==FALSE) {Send TFS} else         {check for change in values of computed TFS with the previously         sent TFS. If change {Send TFS} else {do nothing} }     -   Set TFS IN_CALCULATION==false)     -   If (TFS is sent) {Start TFS_HOLD_DOWN_TIMER}

Upon expiration of TIS_HOLD_DOWN_TIMER:

-   -   Stop and clear TIS_HOLD_DOWN_TIMER     -   If (no new TIS) {do nothing}     -   If (new TIS)         -   Set TIS_IN_CALCULATION==true)         -   Compute TIS using most recent stored TIS and TFS.         -   If (TIS_SIGNAL_SUPPRESS_IF_NO_CHANGE==FALSE) {Send TIS} else             {check for change in values of computed TIS with the             previously sent TIS. If change {Send TIS} else {do nothing}             }         -   Set TIS_IN_CALCULATION==false)         -   If (TIS is sent) {Start TIS_HOLD_DOWN_TIMER}

FIG. 33 is a diagram 3300 illustrating an example where a perpetual exchange of signals might become sustained between two transactive node neighbors.

Upon expiration of TFS_HOLD_DOWN_TIMER:

-   -   Stop and clear TFS_HOLD_DOWN_TIMER     -   If (no new TFS) {do nothing}     -   If (new TFS)         -   Set TFS IN_CALCULATION==true)         -   Compute TFS using most recent stored TIS and TFS.         -   If (TFS_SIGNAL_SUPPRESS_IF_NO_CHANGE==FALSE) {Send TFS} else             {check for change in values of computed TIS with the             previously sent TFS}         -   Set TFS IN_CALCULATION==false)         -   If change {Send TFS} else {do nothing}         -   If (TFS is sent) {Start TFS_HOLD_DOWN_TIMER}

Upon expiration of the WATCHDOG_TIMER:

-   -   If (WATCHDOG_TIMER_SIGNAL_GEN_ALWAYS_ON==TRUE) {         -   If (local_conditions_change==TRUE)∥(TIS/TFS is not computed             in this period) {             -   Recompute TIS/TFS}         -   Send TIS; Send TFS; Start TIS_HOLD_DOWN_TIMER; Start             TFS_HOLD_DOWN_TIMER}     -   If (WATCHDOG_TIMER_SIGNAL_GEN_ALWAYS_ON==FALSE) {         -   If (local_conditions_change==FALSE) && (we sent TIS/TFS in             the last transactive signal interval) {             -   Do nothing}         -   Else {             -   Recompute TIS/TFS             -   Send TIS; Send TFS; Start TIS_HOLD_DOWN_TIMER; Start                 TFS_HOLD_DOWN_TIMER}}

TABLE 22 Dictionary of Exemplary Timing Attributes Recommended at a Transactive Node to Facilitate Exchange of Transactive Signals between Transactive Neighbors Range of No. Attribute Name Description Role Type Format values B1 TIS_TIMER Started upon Allows for arrival Single real — The value 0 receipt of the of more TIS number. (zero) first TIS in signals before disables the the current processing TIS_TIMER. update occurs. Helps Default interval (See retard value: 12 s. 9-Update successive Frequency). transmissions of TIS signals. B2 TFS_TIMER Started upon Allows for arrival Single real — The value 0 receipt of the of more TFS number. (zero) first TFS in signals before disables the the current processing TFS_TIMER. update occurs. Helps Default interval (See retard value: 12 s. 9-Update successive Frequency). transmissions of TFS signals. B3 TIS_IN_ If set, Certain actions Binary — 0-Not busy CALCULATION indicates that are to be condition calculating the prevented flag. TIS. transactive during this time Dimensionless. 1-Busy node is to avoid calculating engaged in corrupting TIS. recalculating calculated its TIS value. signals. B4 TFS_IN_ If set, Certain actions Binary — 0-Not busy CALCULATION indicates that are to be condition calculating the prevented flag. TFS. transactive during this time Dimensionless. 1-Busy node is to avoid calculating engaged in corrupting TFS. recalculating calculated its TFS signals. values. B5 TIS_HOLD_ Started upon Used to Single real — Use of 0 DOWN_TIMER sending a suppress number. (zero) as the TIS. transmission of Units: s. value Successive excessive TIS disables this TIS may not messages. timer. be Default: 30 s. transmitted Timer by this transactive node until duration after this should be timer has shorter than expired. the update interval indicated by 9-Update Frequency attribute. B6 TFS_HOLD_ Started upon Used to Single real — Use of 0 DOWN_TIMER sending a suppress number. (zero) as the TFS. transmission of Units: s. value Successive excessive TFS disables this TFS may not messages. timer. be Default: 30 s. transmitted Timer by this duration transactive should be node until shorter than after this the update timer has interval expired. indicated by 9-Update Frequency attribute. B7 WATCHDOG_ An event Actions, like the Single real — Use of 0 TIMER occurs at this transmission of number. (zero) as the interval transactive Units: s. value duration and signals and the disables this is aligned sending of asset timer (e.g., with the control no action transitions recommendations may be from one to asset induced by a update systems, may watchdog- interval into be configured to timer event. the next. occur each time If assigned a (In some the watchdog non-zero embodiments, timer expires. value, this the During testing, duration watchdog the watchdog should be time is timer duration longer than aligned with may be any of the the update shortened to attributes B1- interval, but speed the rate TIS Timer, that need not at which B2-TFS be the case observations Timer, B5- in general. If may be taken TIS Hold the watchdog and thereby Down Timer, timer is facilitate testing or B6-TFS further of a transactive Hold Down specified to node. Timer. (If this occur n is not the seconds case, then (e.g., 15 watchdog seconds) timer events prior to the will occur start of the prior to next update calculating interval, it and can be more transmitting useful to transactive induce signals, and transmission watchdog of transactive event signals and induced asset control actions may actions that accumulate.) are relevant Default for the value: Set pending equal to the update duration of interval.) the update interval, which is 300 s for the Demonstration. B8 WATCHDOG_ This attribute If set true, this Logical Logic 0-False- TIMER_ specifies attribute will condition transactive SIGNAL_GEN_ whether cause flag: true/ signals are ALWAYS_ON transactive transactive false. sent when signals are to signals to be the watchdog be transmitted upon timer transmitted the occurrence duration or not when of a watchdog expires only a watchdog timer event; if if timer event set false, only corresponding occurs. the type of corresponding transactive transactive signal was signals that not sent were not sent by during the this transactive expiring node during the watchdog expiring timer watchdog time duration. interval are to be 1-True- transmitted. the default See transactive condition- neighbor transmit TIS connection and TFS attributes 59- transactive TIS Sent Flag signals upon and 60-TFS the Sent Flag expiration of attributes. the watchdog timer regardless of whether any transactive signals were transmitted during the watchdog timer duration that is expiring. B9 TIS_SIGNAL_ This attribute This attribute Logical Logic. 0-False- SUPPRESS_IF_ controls TIS and the related condition the NO_CHANGE generation. If attribute B10 flag: true/ differences this attribute can reduce the false. between is set true, numbers of newly the redundant calculated transactive transactive and prior control node signals transmitted will compare transmitted by TIS signals computed this transactive are not signal values node. There is relevant. to the little value in 1-True- respective sending default value- previous transactive the corresponding signals that are difference transactive virtually identical between a signal sent to ones that newly and will not have already calculated send another been sent. and prior TIS if the This attribute transmitted values show works in TIS should be no significant conjunction with compared, changes. attributes C1- and the Default value C4 (see newly is true. Appendix C calculated concerning the TIS will be relaxation stop transmitted criterion) and only if the attribute B8. (In difference some was found to embodiments, if be significant. B9 or B10 are See true, the Appendix C respective and transactive attributes C1- signals will not C4 for a be sent unless metric of they are significance. significantly different from the last ones sent, regardless of the condition of flag B8.) B10 TFS_SIGNAL_ This attribute This attribute Logical Logic. 0-False- SUPPRESS_IF_ controls TFS and the related condition the NO_CHANGE generation. If attribute B9 can flag: true/ differences this attribute reduce the false. between is set true, numbers of newly the redundant calculated transactive transactive and prior control node signals transmitted will compare transmitted by TFS signals computed this transactive are not signal values node. There is relevant. to the little value in 1-True- respective sending default value- previous transactive the corresponding signals that are difference transactive virtually identical between a signal sent to ones that newly and will not have already calculated send another been sent. and prior TFS if the This attribute transmitted values show works in TFS should no significant conjunction with be changes. attributes C1- compared, Default value C4 (see and the is true. Appendix C newly concerning the calculated relaxation stop TFS will be criterion) and transmitted attribute B8. only if the difference was found to be significant. See Appendix C and attributes C1- C4 for a metric of significance.

6.1.19 Transactive Signal Relaxation Stop Criterion 6.1.19.1 Purpose

In certain embodiments, transactive nodes periodically send their transactive signals to their neighbors. The timing of this responsibility has recently been specified and will become included in reference code implementations of the transactive node model algorithm (TNMA). The timing specification references a relaxation stop criterion based upon changes observed between the present signal and the most recent prior signal that has been calculated and sent by this transactive node. If the signals are found to have not changed much, this transactive node should not send its calculated signal again during the present update interval.

The purpose of this section is to state the criterion by which a transactive node may discern whether it should continue to send out its calculated transactive signals or not during the present update interval.

6.1.19.2 Relaxation Stop Criterion

A relaxation stop criterion can be used under the following assumptions:

-   -   1. Near-term predictions should be known with accuracy.         Prediction inaccuracies and perturbations are somewhat more         acceptable far into the future because one will have many         opportunities to iterate and improve those distant predictions.         Near-term predicted inaccuracies and events may necessitate         additional iterations until the system relaxes to a steady,         negotiated solution.     -   2. A prediction error decreases inversely proportional to some         constant to the power of the number of iterations. The constant         represents the improvement expected from each iteration and will         usually range from [1,2+). If the constant is set to the         conservative value 1, one expects the error not at all to be         improved by iteration. If the number is set to 2, one expects         that each successive iteration should halve the error. It is         conceivable that over-relaxation solutions could allow for         constants larger than 2.     -   3. The impact of an inaccurate prediction is roughly         proportional to the predicted interval's duration.

For each future interval s, define error ε_(s) as the absolute difference between the present estimate of the value V_(s)(k) and the prior estimate of the value V_(s)(k−1).

ε_(s) =|V _(s)(k)−V _(s)(k−1)|  (Eq. C1)

The criterion should be applied consistently to either the value itself or to a relative representation of the value, which further results in dividing the result in Eq. C1 by the absolute value of V_(s)(k).

Each error ε_(s) should be factored by a corresponding weighting factor W_(s) to account for the impacts of the duration of each future interval s and the number of iterations that may be reasonably performed on the prediction.

$\begin{matrix} {W_{s} = \frac{D_{s}}{\gamma^{{({t_{s} - t_{0}})}/D}}} & \left( {{{Eq}.\mspace{14mu} C}\; 2} \right) \end{matrix}$

In Eq. C2, D_(s) is the time duration of interval s, and γ is a constant [1,2+) that represents the effectiveness of each iteration, as was described in bullet #2 above. The term (t_(s)−t₀)/D represents the number of iterations that could reasonably be completed if iterations are conducted after every D constant time interval between the start of the predicted interval t_(s) and the present time t₀. For example, the system can update its calculations every 5 minutes, so one might naturally expect over 12 opportunities for the solution to iteratively converge every hour.

The overall relaxation stop criterion may then be stated as a constant E that is proportional to the sum of all the weighting factors. The proportionality constant K represents a conservative “typical” error ε_(s) that would be deemed acceptable. Some trial and error may occur to select the proportionality constant K that will result in an acceptable number of iterations.

The time series has been iterated adequately when the weighted sum of errors are less than the constant E, in which case iterations should be halted. If, however, the weighted sum of errors is greater than or equal to the constant E, then additional iteration should be conducted until errors satisfy the criterion.

$\begin{matrix} {E = {{K{\sum\limits_{S}^{\;}\; W_{s}}}\overset{?}{>}{\sum\limits_{S}^{\;}\; {W_{s} \cdot ɛ_{s}}}}} & \left( {{{Eq}.\mspace{14mu} C}\; 3} \right) \end{matrix}$

The complete criterion is stated in Eq. C4.

$\begin{matrix} {E = {{K \cdot {\sum\limits_{s = 0}^{S}\; \frac{D_{s}}{\gamma^{{({t_{s} - t_{0}})}/D}}}}\overset{?}{>}{\sum\limits_{s = 0}^{S\;}\; \frac{D_{s} \cdot ɛ_{s}}{\gamma^{{({t_{s} - t_{0}})}/D}}}}} & \left( {{{Eq}.\mspace{14mu} C}\; 4} \right) \end{matrix}$

An example has been worked through in Appendix A using three different values of constant γ. The example uses a set of intervals from the Demonstration of the type that will be used for its transactive signals. The weighting factors for the series of intervals and at the three example values of constant γ have been plotted in graph 3400 of FIG. 34.

Large gamma (e.g. γ=2.0) is shown to discount the importance of error in future predictions more than small values of gamma (e.g., γ=1.0625). The jagged curve reflects that long interval durations are weighted more than short ones, which is relevant for the Demonstrations intervals that become successively longer after the 12^(th), 32^(nd), 50^(th), and 54 ^(th) intervals. The impact of distant future weightings may become negligibly small.

FIG. 34 is a graph 3400 showing weighting factors for a set of Demonstration intervals (IST₀=0:00) using three different values of constant γ.

TABLE 23 Example Weighting Factors W_(s) for a Sample Series of Intervals and for Three Different Gamma Values Sam- D_(s) t_(s)-t_(o) ple (min- (min- W_(s) (#) utes) utes) γ = 2 γ = 1.25 γ = 1.0625 0 5 1/0/00 0:00 0 5.00E+00 5.00E+00 5.00E+00 1 5 1/0/00 0:05 5 2.50E+00 4.00E+00 4.71E+00 2 5 1/0/00 0:10 10 1.25E+00 3.20E+00 4.43E+00 3 5 1/0/00 0:15 15 6.25E−01 2.56E+00 4.17E+00 4 5 1/0/00 0:20 20 3.13E−01 2.05E+00 3.92E+00 5 5 1/0/00 0:25 25 1.56E−01 1.64E+00 3.69E+00 6 5 1/0/00 0:30 30 7.81E−02 1.31E+00 3.48E+00 7 5 1/0/00 0:35 35 3.91E−02 1.05E+00 3.27E+00 8 5 1/0/00 0:40 40 1.95E−02 8.39E−01 3.08E+00 9 5 1/0/00 0:45 45 9.77E−03 6.71E−01 2.90E+00 10 5 1/0/00 0:50 50 4.88E−03 5.37E−01 2.73E+00 11 5 1/0/00 0:55 55 2.44E−03 4.29E−01 2.57E+00 12 15 1/0/00 1:00 60 3.66E−03 1.03E+00 7.25E+00 13 15 1/0/00 1:15 75 4.58E−04 5.28E−01 6.04E+00 14 15 1/0/00 1:30 90 5.72E−05 2.70E−01 5.04E+00 15 15 1/0/00 1:45 105 7.15E−06 1.38E−01 4.20E+00 16 15 1/0/00 2:00 120 8.94E−07 7.08E−02 3.50E+00 17 15 1/0/00 2:15 135 1.12E−07 3.63E−02 2.92E+00 18 15 1/0/00 2:30 150 1.40E−08 1.86E−02 2.43E+00 19 15 1/0/00 2:45 165 1.75E−09 9.51E−03 2.03E+00 20 15 1/0/00 3:00 180 2.18E−10 4.87E−03 1.69E+00 21 15 1/0/00 3:15 195 2.73E−11 2.49E−03 1.41E+00 22 15 1/0/00 3:30 210 3.41E−12 1.28E−03 1.18E+00 23 15 1/0/00 3:45 225 4.26E−13 6.53E−04 9.80E−01 24 15 1/0/00 4:00 240 5.33E−14 3.35E−04 8.17E−01 25 15 1/0/00 4:15 255 6.66E−15 1.71E−04 6.81E−01 26 15 1/0/00 4:30 270 8.33E−16 8.77E−05 5.68E−01 27 15 1/0/00 4:45 285 1.04E−16 4.49E−05 4.74E−01 28 15 1/0/00 5:00 300 1.30E−17 2.30E−05 3.95E−01 29 15 1/0/00 5:15 315 1.63E−18 1.18E−05 3.29E−01 30 15 1/0/00 5:30 330 2.03E−19 6.03E−06 2.74E−01 31 15 1/0/00 5:45 345 2.54E−20 3.09E−06 2.29E−01 32 60 1/0/00 6:00 360 1.27E−20 6.32E−06 7.63E−01 33 60 1/0/00 7:00 420 3.10E−24 4.34E−07 3.69E−01 34 60 1/0/00 8:00 480 7.57E−28 2.98E−08 1.78E−01 35 60 1/0/00 9:00 540 1.85E−31 2.05E−09 8.60E−02 36 60 1/0/00 10:00 600 4.51E−35 1.41E−10 4.16E−02 37 60 1/0/00 11:00 660 1.10E−38 9.68E−12 2.01E−02 38 60 1/0/00 12:00 720 2.69E−42 6.65E−13 9.70E−03 39 60 1/0/00 13:00 780 6.57E−46 4.57E−14 4.69E−03 40 60 1/0/00 14:00 840 1.60E−49 3.14E−15 2.26E−03 41 60 1/0/00 15:00 900 3.92E−53 2.16E−16 1.09E−03 42 60 1/0/00 16:00 960 9.56E−57 1.48E−17 5.28E−04 43 60 1/0/00 17:00 1020 2.33E−60 1.02E−18 2.55E−04 44 60 1/0/00 18:00 1080 5.70E−64 7.01E−20 1.23E−04 45 60 1/0/00 19:00 1140 1.39E−67 4.82E−21 5.96E−05 46 60 1/0/00 20:00 1200 3.40E−71 3.31E−22 2.88E−05 47 60 1/0/00 21:00 1260 8.29E−75 2.27E−23 1.39E−05 48 60 1/0/00 22:00 1320 2.02E−78 1.56E−24 6.72E−06 49 60 1/0/00 23:00 1380 4.94E−82 1.07E−25 3.25E−06 50 360 1/1/00 0:00 1440 7.24E−85 4.43E−26 9.41E−06 51 360 1/1/00 6:00 1800 1.53E−106 4.66E−33 1.20E−07 52 360 1/1/00 12:00 2160 3.25E−128 4.91E−40 1.52E−09 53 360 1/1/00 18:00 2520 6.87E−150 5.17E−47 1.93E−11 54 1440 1/2/00 0:00 2880 5.82E−171 2.18E−53 9.84E−13 55 1440 1/3/00 0:00 4320 1.17E−257 2.68E−81 2.57E−20 56 1440 1/4/00 0:00 5760 — 3.30E−109 6.72E−28

6.1.19.3 Additional Transactive Node Attributes where a Relaxation Stop Criterion is Employed

Table 24 specifies four additional transactive node attributes that can be used if a transactive node is to employ the relaxation stop criterion as it has been introduced in this appendix. These attributes can be assumed to be assignable at the transactive-node level. It is conceivable that this criterion (or another) and its attributes may in the future be configured differently for each transactive neighbor connection.

TABLE 24 Dictionary of the Relaxation Stop Criterion Attributes that may be Configured at a Transactive Node Range of No. Attribute Name Description Role Type Format values C1 Relaxation This is one of This Single real — Typically, [0, Stop Criterion the two parameter number. 1). Proportionality parameters represents a This Default Threshold- that determine maximum parameter's value: TIS whether a allowed units of 0.0005. calculated average measure are Set to 0.0 Output TIS absolute effectively the for time series difference same as for maximum has between the Output iterations. adequately consecutively TIS: $/kWh. Set to 1 to relaxed to a calculated practically steady Output TIS eliminate solution at this members iterations transactive TIS_(n). altogether. node. The Empirically This magnitudes of set this parameter is attributes C1 parameter's the and C3 value to proportionality together achieve the constant K affect how desired that is shown similar Output numbers of in Eq. C4. TIS time Output TIS series should being be for us to transmitted stop iterating from this and again transactive transmitting node. the Output TIS. The magnitude of this parameter affects how many times an Output TIS will be sent to transactive neighbors by this transactive node. C2 Relaxation This is one of This Single real — Typically, [0, Stop Criterion the two parameter number. 100,000). Proportionality parameters represents a This Default Threshold- that determine maximum parameter's value: 100. TFS whether a allowed units of Set to 0.0 calculated average measure are for Output TFS absolute effectively the maximum time series difference same as for iterations. has between an Output Set to adequately consecutively TFS: Average 100,000 to relaxed to a calculated kW. practically steady Output TFS eliminate solution at this members iterations transactive TFS_(n). altogether. node. The Empirically This magnitudes of set this parameter is attributes C2 parameter's the and C4 value to proportionality together achieve the constant K affect how desired that is shown similar Output numbers of in Eq. C4. TFS time Output TFS series should being be for us to transmitted stop iterating from this and again transactive transmitting node. the Output TFS. The magnitude of this parameter affects how many times an Output TFS will be sent to transactive neighbors by this transactive node. C3 Relaxation This is one of This Single real — Range: [1, Stop Criterion the two parameter number. 2). Gamma parameters represents This Default: 1.0 Parameter- that determine the relative parameter is Empirically TIS whether a impact of a dimensionless. set this calculated sample's parameter's Output TIS duration and value to time series a sample's achieve the has distance into desired adequately the future as numbers of relaxed to a successive Output TIS steady Output TIS being solution at this values are transmitted transactive being from this node. compared. transactive This The node. parameter is magnitudes of the constant Y attributes C1 that is shown and C3 in Eq. C4. together affect how similar Output TIS time series should be for us to stop iterating and again transmitting the Output TIS. The magnitude of this parameter affects how many times an Output TIS will be sent to transactive neighbors by this transactive node. C4 Relaxation This is one of This Single real — Range: [1, Stop Criterion the two parameter number. 2). Gamma parameters represents This Default: 1.0 Parameter- that determine the relative parameter is Empirically TFS whether a impact of a dimensionless. set this calculated sample's parameter's Output TFS duration and value to time series a sample's achieve the has distance into desired adequately the future as numbers of relaxed to a successive Output TIS steady Output TFS being solution at this values are transmitted transactive being from this node. compared. transactive This The node. parameter is magnitudes of the constant Y attributes C2 that is shown and C4 in Eq. C4. together affect how similar Output TIS time series should be for us to stop iterating and again transmitting the Output TIS. The magnitude of this parameter affects how many times an Output TIS will be sent to transactive neighbors by this transactive node.

6.2 Appendix B Transactive Node Toolkit Framework 6.2.1 Terms

This section will sometimes make reference to the following terms, whose nonlimiting definitions are also given below. These definitions do not necessarily apply in all instances and may vary depending on the context.

elastic load Within the toolkit framework, the change in electrical load that is expected as responsive asset systems respond to the transactive incentive signal (TIS). Within the toolkit framework, information about elastic load will be stored into and available from the Toolkit Response Function Output Parameter Buffer. inelastic load Electrical load that is not responsive to the transactive incentive signal (TIS) at a transactive node. In certain implementations, it is recommended that inelastic load should also include the predicted load from responsive asset systems if they were to not respond to the TIS. Within the toolkit framework, information about inelastic load will be stored into and available from the Inelastic Load Prediction Buffer. input transactive input A transactive feedback signal (TFS) that has been received feedback signal TFS from a transactive neighbor as an input to the set of calculations that is to be conducted at a transactive node at the updated frequency. input transactive input A transactive incentive signal (TIS) that has been received from incentive signal TIS a transactive neighbor as in input to the set of calculations that is to be conducted at a transactive node at the update frequency. interval start IST An attribute of transactive signals. The series of future times time that define the starting times of members of set of future time intervals. The duration of each interval is defined by the time between two consecutive interval start times. other local OLC A broad set of information and data that will be inputs into the conditions many functions and processes that is to be performed at transactive nodes. This set excludes transactive signals. output output A transactive feedback signal (TFS) object output from the transactive TFS calculations that are to be conducted at a transactive node at feedback signal the update interval. A transactive node prepares an output TFS that predicts the average power to be exchanged with a transactive neighbor into the future. output output A transactive incentive signal (TIS) object output from the transactive TIS calculations that are to be conducted at a transactive node at incentive signal the update interval. responsive asset A system within the control of a transactive node that will system change its consumption or generation in response to the transactive node's transactive incentive signal (TIS) and other local conditions. toolkit The toolkit framework, toolkit function libraries, the set of toolkit functions, and/or associated documentation. toolkit The general functionality and responsibilities at any transactive framework node. The flow in which high-level and more specific toolkit functions are coordinated and accomplished. toolkit function An individual functional capability that may be implemented at a transactive node. There are two main types of toolkit functions-incentive and response. toolkit function A set of toolkit functions available to implementers. library Implementers select toolkit functions from this library that can be instantiated and interoperably applied at their transactive node. toolkit load A toolkit function inserted into the toolkit framework process function 8. Calculate Toolkit Resource and Incentive Function that calculates energy and energy cost for a resource and other cost components and incentives that will be used in the formulation of the transactive incentive signal. toolkit resource A toolkit function inserted into the toolkit framework process and incentive 6. Calculate Toolkit Load Function that calculates the function predicted inelastic load and changes in elastic load components of the entire load at a transactive node. transactive TC A negotiated form of power grid control that uses price-like control incentive and feedback signals. transactive TCS A distributed system that employs transactive control and control and coordination. coordination system transactive TFS One of the major transactive signals employed by feedback signal embodiments of a transactive control and coordination system. A transactive node's reporting of the expected average power to be transferred between two transactive neighbors during intervals over the next several days. transactive TIS One of the major transactive signals employed by incentive signal embodiments of a transactive control and coordination system. A transactive node's reporting of the anticipated delivered cost of electrical power at its location at intervals over the next several days. transactive TS Either the transactive incentive signal (TIS) or transactive signal feedback signal (TFS). transactive Transactive nodes that exchange electrical energy between neighbors them and therefore also exchange transactive signals. transactive node TN A defined location of the transactive control and coordination system that has agreed to exchange transactive incentive signals (TIS) and transactive feedback signals (TFS) with its transactive neighbors. transactive node TNMA A module of software where the functionality of transactive model algorithm control is created for a transactive node. The Demonstration chooses to apply this term to software modules that serve this function. transactive node A formal construct possessing attributes that may be used to object define the state of a transactive node and the transition between those states. Transactive TNOM The model of the states of the transactive node object and the node object functions by which it moves from one state to another. The model TNOM includes the model of a transactive node object's configuration. update Reciprocal of the update interval. The update frequency should frequency be made configurable to support future implementations and testing. update interval Relatively short time interval between consecutive updates of the TIS and TFS at each transactive node.

6.2.2 Introduction

A transactive node represents a predetermined component or region within an electric power grid at which electrical energy may be generated, consumed, imported, or exported. In principle, the transactive node construct will be scalable and similarly applicable to from small, end-use equipment (e.g., a distribution transformer, residential thermostats) to large regions (e.g., the boundary of an electric utility). A transactive node includes an agent of sorts (e.g., a computer and its software applications) that orchestrates each transactive node's responsibilities to:

1. economically balance energy 2. incentivize energy consumption or generation 3. activate its own responsive generation and load resources 4. exchange both transactive incentive signals (TIS) and transactive feedback signals (TFS) with each of its neighboring transactive nodes.

The two transactive signals—the transactive incentive signal (TIS) and transactive feedback signal (TFS)—reveal the predicted local delivered cost of electric energy and the predicted use of a TN to exchange electrical energy with its neighbors, given the value of the TIS and other predicted local conditions. (While this document refers often to pairs of TIS and TFS signals, the two signals need not necessarily always be received and sent together and simultaneously. Instead, the signals can be decoupled so that they may be sent and received separately.)

These functional behaviors should be designed into the transactive control and coordination system. Depending on its complexity, memberships, and location in its power grid, a transactive node may assume all, some, or practically none of the responsibilities to be described in this document. The toolkit function library construct is one way to organize and teach the responsibilities of a TN to those who would wish to define a transactive node and have their transactive node enter into an existing transactive control and coordination system. The toolkit library should not only hasten the adoption and implementation of transactive control, but it should also standardize implementations of transactive control so that the building blocks components will be more interoperable. The toolkit library should be available to implementers who may choose from and learn from others' experiences and practices. The template for toolkit library functions anticipates providing reference implementation code with which implementers may jump start their instantiation of similar functions.

The functional responsibilities of a transactive node will be described at two levels of the toolkit:

1. Toolkit framework—the high-level computational structure that provides basic transactive control functionality of transactive nodes and that calls upon specific toolkit library functions to enact the functionality of specific incentives and assets. 2. Toolkit library functions—the specific functions that account for resource, enact incentives and plan asset responses at transactive nodes where these specific functions have been implemented and are relevant. Applicable toolkit library functions are called upon and acted upon within the toolkit framework.

6.2.3 Toolkit Framework

The toolkit framework is a high-level structure for the inputs, functions, processes, and outputs that define transactive control functionality at a transactive node. The toolkit framework will probably be found to encompass the high-level functional responsibilities of the transactive node model algorithm (TNMA) module.

(This document primarily addresses the algorithmic functionality of a transactive node and its responsibilities toward management of electrical energy. This document may facilitate, but does not intend to specify, functionality toward system management, timing, and data collection that are better addressed within the transactive node's object model.)

FIG. 35 is a flowchart 3500 that shows the flow of information during each update interval (e.g., 5-minute update interval) at the rate of the update frequency. This is a functional flow, not necessarily a recommendation for how a developer will construct the software program. The blocks in this diagram represent functions and processes. The distinction between “functions” and “processes” may be somewhat subjective, but a process will have been defined to have multiple sub-functions and/or sub-processes. Blocks of FIG. 35 shown with bold outlines are processes, known to be composed of at least two sub-functions or processes.

The flow of information in FIG. 35 is indicated by solid arrows. Information is processed predominantly downward through the diagram, which makes the diagram useful for understanding functional, sequential interdependencies. Other logical flow control and dependencies are shown by dashed arrows.

Information buffers appear in several of the information flow paths. These buffers are available to be mined by data collection processes and might be made accessible to the system management level. (These buffers, if defined as part of a standard transactive node definition, can be used as a point of observability for testing. In addition, the option of preloading the buffers may be useful for testing (especially if only the 5-minute update frequency is available).) The buffers also provide recent information that may be used if any prior function or process should fail to promptly complete its responsibilities or provide its output information. The flow in this diagram has been greatly simplified by the assumption that any buffered historical information is available to be used by any other function or process at this transactive node.

As part of its data collection design for transactive data, a number of buffers can be used. For example, in the illustrated embodiment in FIG. 35, five buffers are identified, the contents of which compose a sufficient snapshot of the calculations that have been completed by the toolkit framework and its toolkit functions at a transactive node. The five buffers are those into which calculation products are to be sent: Resource Schedule and Cost Buffer, Output TIS Buffer, Output TFS Buffer, Inelastic Load Prediction Buffer, and Elastic Load Prediction Buffer. The freshest, unique buffer records from these five buffers are specified to be collected after any transactive signal has been calculated and sent to a transactive neighbor. The sampling of these five buffers is sufficient in the sense that the outputs from each toolkit resource and incentive function and each toolkit load function are revealed, the TIS and TFS transactive signals that have been transmitted from this transactive node are revealed, and the magnitudes of transactive signals that have been received may be inferred, if not perfectly known. Alternatively, signal timing and data collection can be initiated by changes that have been detected, not by rigid timers.

The following processes and functions are referenced in FIG. 35 and will be described in the next sections. Defined functions, processes, and specially defined inputs and outputs of the functions and processes will be shown in bold font in this document.

1. Receive Transactive Signals

1.1. Read TIS and TFS from Transactive neighbor

1.2. Check Authentication and Security

1.2.1. Interact with System Management (Security)

1.3. Check Validity of Transactive Signals

1.3.1. Interact with System Management (Validity) 1.4. Update Input Transactive Signal Buffer for this Transactive neighbor

2. Calculate New Transactive Signal Intervals 2.1. Read Present Time 2.2. Calculate First Interval Start Time IST° 2.3. Calculate 5-Minute Interval Start Times 2.4. Calculate 15-Minute Interval Start Times 2.5. Calculate 1-Hour Interval Start Times 2.6. Calculate 6-Hour Interval Start Times 2.7. Calculate 1-Day Interval Start Times

2.8. Calculate Interval Durations from Interval Start Times

3. Formulate TIS 3.1. Refresh Default Output TIS 3.2. Calculate Total Costs of Non-Transactive Energy Generation and Imports

3.3. Calculate Total Cost of Energy Imported from Transactive nodes

3.4. Calculate Total Capacity Cost/Incentives 3.5. Calculate Total Infrastructure Cost/Incentive 3.6. Calculate Total Other Cost/Incentive 3.7. Calculate Output TIS 3.8. Calibrate/Normalize TIS 3.9. Interpolate Intervals Service Functions 4. Formulate TFS 4.1. Interpolate Intervals Service Functions 4.2. Predict Net Resource Surplus or Shortage 4.3. Disaggregate Net Resource Surplus or Shortage 4.4. Refresh Default Output TFS 5. Sum Total Predicted Load 5.1. Interpolate Intervals Service Functions 5.2. Sum Inelastic Load 5.3. Sum Change in Elastic Load 5.4. Sum Total Inelastic and Change in Elastic Load 5.5. Refresh Predicted Total Inelastic and Elastic Load 6. Calculate Applicable Toolkit Load Functions 6.1. Interpolate Intervals Service Functions 6.2m Toolkit Load Function 6.3 Refresh Predicted Inelastic and Elastic Loads

7. Send Transactive Signals (Defined only functionally at a high level)

8. Calculate Applicable Toolkit Resource and Incentive Functions

9. Control Responsive Asset Systems (Defined only functionally at a high level)

10. Sum Total Predicted Resources 10.1. Interpolate Intervals Service Functions 10.2. Sum Total Predicted Resource 10.3. Refresh Predicted Total Resource 11. Control Responsive Resource

The next sections will describe examples of the functions in the above list. The sections below are demarcated by the function numbers set forth in the list above, and are not to be confused with the section numbering used outside of this appendix.

1. Receive Transactive Signals

Purpose:—Transactive signals are signals to be communicated between transactive nodes in a transactive control and coordination system. It is through transactive signals that transactive nodes share their temporal and locational costs and thirsts for electrical energy. Transactive incentive signals (TIS) and transactive feedback signals (TFS) should be received from the transactive node neighbors at the update frequency, which happens to be once every 5 minutes for the Demonstration.

This function includes technical validation of received signals to ensure that they were properly formed and that their values are within acceptable norms. Validation is not yet a high priority, and validation processes probably do not need to be standardized across all transactive nodes. If an invalid signal is detected, it should be flagged. Additional actions may be taken to notify or alert targeted system operators and reduce the impacts from potentially misleading signals.

Applicability: This function should be completed by a transactive node at least once during an update interval. If this function fails, functions and processes of the toolkit framework that use an input transactive signal should revert to buffered historical signals.

Sub-Functions and Sub-Processes: The following sub-functions are iteratively completed until the input transactive signals from transactive neighbors have been received.

1.1 Read TIS and TFS from a Transactive neighbor—Function by which the TIS and TFS from a transactive neighbor is to be received. Most generally, the implementation details by which this sub-function is to be accomplished should be negotiated by pairs of transactive neighbors that will exchange transactive signals.

1.2 Check Authentication and Security—Functional block (or blocks) for signals like transactive signals that are to be conveyed through the transactive control and coordination system. The actual functional implementation details for security functions may differ from one implementation to another, but general descriptions for this block should be documented if they are applicable to any transactive node.

1.2.1 Interact with System Management (Security)—Actions that are to be taken if Check Authentication and Security function fails to authenticate a transactive signal or detects an insecurity. The input transactive signals are terminated if they cannot be authenticated or if security violations are suspected. Actions may include notifications and alerts that are to be conveyed by the system management layer. Specific actions of this function may differ by implementation.

1.3 Check Validity of Transactive Signals—Functional block (or blocks) by which the structure or contents of a transactive signal may be tested against expected and reasonable structure and content. Examples of checks on the structure of the signals could include verification of adherence to an XML schema, an expected number of future intervals, or the ordering of a series within the signal. An example of a content check would be verification that a signal's values are between stated maximum and minimum values.

1.3.1 Interact with System Management (Validity)—Actions that are to be taken if the Check Validity function fails validate transactive signals. The input transactive signals are terminated and not used or stored if they cannot be validated. Actions may include notifications and alerts that are to be conveyed by the system management layer. Specific actions of this function may differ by implementation. General functional aspects for this function that should apply to transactive nodes should be documented and implemented. More sophisticated actions may be taken, including reducing the Quality attribute of signals that have questionable validity.

1.4 Update Input Transactive Signal Buffer for this Transactive neighbor—Received transactive signals are saved into the Input Transactive Signal Buffer. The buffer may be as simple as a running (or circular) list of transactive signal pairs that have been received from transactive neighbors. The most recently received pairs or transactive signals from each transactive neighbor are most relevant within this buffered data. A much longer buffered history may be used at transactive nodes that use trending to predict transactive neighbors' responses (e.g., elasticity) or to improve the accuracy of their transactive signal predictions over time.

Inputs:

-   -   Input TIS from a transactive neighbor     -   Input TFS from a transactive neighbor     -   List of transactive neighbors from which transactive signals are         expected to be received as should be known by the transactive         node object and available from the Node State and Status Buffer.         This information in the Node State and Status Buffer can be part         of the transactive node configuration and state available within         the transactive node object model, not temporary “buffer”         information as the name might imply.

Outputs:

-   -   Buffered copies of Input TIS and TFS.     -   Copies of Input TIS and TFS pairs conveyed to a data archive by         the data collection system layer.     -   System management notifications and alerts upon failed security         or validation checks, if such system management functions have         been defined and if this transactive node is obligated to         interact with a system manager.

Dependencies:

-   -   Outputs of this function are used by Resource Schedules and Cost         Buffer.     -   Outputs of this function are used by Formulate TIS.     -   Times at which this transactive node is eligible to receive         transactive signals may be managed or limited by the current         state of the transactive node object, which status is assumed to         be known and available from a Node State and Status Buffer.     -   A set of transactive neighbors should be available from         attributes of the transactive node object, which are assumed to         be known and available from a Node State and Status Buffer.

Notes:

-   -   The TIS and TFS are state objects of the Project-Level         Infrastructure (PLI) transactive control and coordination         system. This process expects and checks that transactive signals         are being received with the specified content and structure,         which may be further enforced through the use of, for example,         accepted XML schema.     -   Considerable tolerance should be built into this function to         coordinate with neighboring transactive nodes and their         readiness to release their transactive signals. The function         should be tolerant for when transactive signals are not         received, or are not received early enough to influence the         present update iteration.     -   When an incomplete set of transactive signals is received by a         transactive node, the transactive node should rely upon buffered         historical information from previous iterations. Unless the         power grid's predicted future has changed dramatically, the         buffered signals will remain good predictions until input         transactive signals are received.     -   A Node State and Status Buffer has been established within the         toolkit framework to ensure that it has information it may used         concerning timing, transactive neighbors, and other status         information concerning activities of the transactive node         object.     -   This function interacts with cyber security subsystems. It is         assumed here that authentication and other cyber-security tests         have been conducted during signal transport or upon signal         receipt.     -   This function potentially interacts with system management if         invalid signals are detected or if notifications or alerts         should be conveyed through the system for any reason concerning         signals that have been, or should have been, received.     -   This function potentially depends upon assumptions and         functionality within the state transition diagrams of the         transactive node state, which design is presently incomplete. It         has been assumed that the transactive node state diagram has         provided states where toolkit framework functionality may (or         may not) be conducted. Otherwise, it has been assumed that that         design of the transactive node state transitions does not         encroach on the functional responsibilities of the toolkit         framework.

FIG. 36 is a flowchart 3600 illustrating an exemplary receive transactive incentive signal process.

2. Calculate New Transactive Signal Intervals

Purpose: Calculate the new interval start time (IST) time series that are attributes of the two transactive signal object types that are to be formulated and conveyed throughout the transactive control and coordination system. See SubAppendix A: Interval Start Time Series Definition for details about an example IST time series and how the series is calculated.

Applicability: This function should be completed by transactive nodes at the update frequency. In particular implementations, an update frequency of once every 5 minutes is used, though other intervals can be used.

Sub-Functions and Sub-Processes: The sub-function steps will be described along with this introduction to the sub-functions. Refer to SubAppendix A for additional details and examples.

2.1 Read Present Time—the present time is locally maintained at each transactive node and should be read near the beginning of each iteration. The present time and representations of time are to be maintained using the UTS standard.

2.2 Calculate First Interval Start Time IST₀—to calculate IST₀, round the present time up to the nearest 5-minute interval.

2.3 Calculate 5-Minute Intervals Start Times—to calculate IST₂ through IST₁₁, add 5 minutes to the prior IST.

2.4 Calculate 15-Minute Interval Start Times—to calculate IST₁₂, add 15 minutes to the prior IST₁₁, and round down to a 15-minute interval. To calculate the remaining 15-minute intervals IST₁₃ through IST₃₁, add 15 minutes to the prior IST.

2.5 Calculate 1-Hour Interval Start Times—to calculate IST₃₂, add 1 hour to the prior IST₃₁, and round down to a 1-hour interval. To calculate the remaining 1-hour intervals IST₃₃ through IST₄₉, add 1 hour to the prior IST.

2.6 Calculate 6-Hour Interval Start Times—to calculate IST₅₀, add 6 hours to the prior IST₄₉, and round down to a 6-hour interval. To calculate the remaining 6-hour intervals IST₅₁ through IST₅₃, add 6 hours to the prior IST.

2.7 Calculate 1-Day Interval Start Times—to calculate IST₅₄, add 1 day to the prior IST₅₃, and round down to a 1-day interval. To calculate the remaining 1-day interval IST₅₅, add 1 day to the prior IST₅₄. In certain embodiments, a final IST₅₆ can be appended that will unambiguously define the duration of the final interval. (The final IST does not define a new interval, it simply states the end of the last interval.)

2.8 Calculate Interval Durations from Interval Start Times—the function by which IST interval durations may be discerned from an IST time series is as follows:

2.8.1 Calculate Δt₀—Subtract IST₁−IST₀ to learn the duration of interval Δt₀ that starts at IST₀.

2.8.2 Tentatively Assign Remaining Δt_(n)—successively subtract IST−IST_(n−1) to tentatively assign durations Δt_(n). The duration of Δt₅₅ has been made unambiguous by appending IST₅₆, which is the end of the last interval.

2.8.3 Perform Checks—certain checks may be possible on the structure of the tentative set of IST intervals. In this formulation, both the IST times and interval durations should increase or stay the same as one progresses through the series. The tentative set of intervals should be corrected if it does not pass these local checks. The system management layer may be employed to flag, alert, or announce failed checks, but it is the each local node's responsibility alone to produce and use a correct and accurate set of IST intervals.

Inputs:

-   -   Present time (determines the first interval start time IST₀ for         the new output transactive signals)

Outputs:

-   -   IST time series—Series of interval start times {IST₀, IST₁, . .         . , IST_(N)} to be used in output TIS and output TFS stored into         and available from the Current IST Series Buffer     -   Series of IST interval durations {Δt₀, Δt₁, . . . , Δt_(N)} that         correspond to the N+1 members of the IST series stored into and         available from the Current IST Series Buffer.

Function/Process: The process steps were described above as the sub-functions were being introduced. Refer to SubAppendix A for further details, pseudo code, and examples.

Dependencies:

-   -   The function's output is used by process 3. Formulate TIS     -   The function's output is used by process 4. Formulate TFS

Notes:

-   -   The need for synchronicity is low or does not exist in a         transactive control and coordination system. Therefore, local         time should be accurate only to within several tens of seconds.         This goal should not be particularly challenging to meet.         Regardless, the Demonstration has imposed requirements for and         means to achieve impressive synchronicity across its system.     -   The IST series is an attribute of both the TIS and TFS state         objects.     -   While the current interval start time (IST) time and interval         series are most relevant to the formulation of transactive         signals, many toolkit framework and toolkit library functions         use access to the current IST time and interval series. The         Current IST Series Buffer construct was created to make this         accessibility explicit within the toolkit framework.

FIG. 37 is a flowchart 3700 for an exemplary calculate new transactive signal intervals process.

3. Formulate TIS

Purpose: Process by which the TIS, one of the two transactive signals, is to be formulated at a transactive node. From its predecessors, this process receives parametric information that is used to determine how energy, capacity, infrastructure, and other influences are to be valued during formulation of the output TIS at this transactive node.

Applicability: This process should be completed at the update frequency by transactive nodes. Some of the sub-functions and sub-processes within this process may be trivial or empty at transactive nodes where the sub-functions or sub-processes are not needed.

Sub-Functions and Sub-processes:

3.1 Refresh Default Output TIS—simply retrieve the most recent output TIS from the Output TIS Buffer at this transactive node and refresh its time intervals by submitting it to function 3.10 Interpolate Intervals Service Functions. The resulting output TIS then returned to the Output TIS Buffer to be used by default if for any reason this transactive node does not compute a more current output TIS by the time it is used. This sub-function should be completed early during each duration. This potentially creates a race condition in software unless the update status of the buffer is maintained. Thus, in some embodiments, this should be used as a default value

3.2 Calculate Total Cost of Non-transactive Energy Generation and Imports—for each IST interval, sum the cost of imported and generated energy from sources that are not transactive neighbors at this transactive node. Examples include the costs of energy that is imported into the region from Canada, California, or other entities that are not participating in transactive control. Another example would be bulk generation from a gas generator that is dispatched in ways that are not affected by the region's transactive control and coordination system. The data that feed into this function will come from resource schedules and Incentive Toolkit Functions that are employed at this transactive node. This function becomes trivial and should not be used at transactive nodes that have neither non-transactive imports nor bulk generation.

The output from this function is the sum of products of pairs of energy costs C_(E,a,n) (units: cost per energy) and average generated or imported power {circumflex over (P)}_(G,a,n) (units: average power), weighted by the corresponding IST interval duration Δt_(n) (units: time).

$\begin{matrix} {\sum\limits_{a = 1}^{A}\; {{C_{E,a,n} \cdot {\hat{P}}_{G,a,n} \cdot \Delta}\; t_{n}}} & \left( {{Sub}\text{-}{Function}\mspace{14mu} 3.2} \right) \end{matrix}$

3.3 Calculate Total Cost of Energy Imported from Transactive nodes—for each IST interval, sum the cost of energy that is predicted to be imported from transactive neighbors. At times when energy is to be imported from transactive neighbors, the TIS & TFS from those transactive neighbors should be treated as special cases of imported energy and treated similarly to non-transactive imported energy (e.g., they result in (C_(E), P_(G)) pairs). The cost of energy from a transactive neighbor is that neighbor's TIS. The predicted energy to be imported from that neighbor is the neighbor's TFS at the boundary between that and this transactive node. Exported energy to transactive neighbors should be disregarded in the calculation of the TIS. (In some embodiments, information about exported energy is found in the Resource Schedules and Cost Buffer. In such embodiments, Functions 3.2 and 3.3 can filter the buffer contents to address only imported energy, in which case the Resource Schedules and Cost Buffer is a complete rich source of information for data collection concerning the outputs of Toolkit Resource and Incentive Functions that are being employed at this transactive node.) It is conceivable that a transactive node could import no energy from its transactive neighbors, but the TFS shared with the neighbors should be checked nonetheless. (The prediction of energy to be exchanged to or from a transactive neighbor can be predicted by both neighbors, by one of the neighbors, or some other combination.)

As for sub-function 3.2, the output from this function will continue the sum of products of pairs of energy costs C_(E,a,n) (TIS) (units: cost per energy) and average generated or imported power {circumflex over (P)}_(G,a,n) (TFS) (units: average power), weighted by the corresponding IST interval duration Δt_(n) (units: time).

$\begin{matrix} {\sum\limits_{a = 1}^{A}\; {{C_{E,a,n} \cdot {\hat{P}}_{G,a,n} \cdot \Delta}\; t_{n}}} & \left( {{Sub}\text{-}{Function}\mspace{14mu} 3.3} \right) \end{matrix}$

3.4 Calculate Total Capacity Cost/Incentive—for each IST interval, sum the costs that are functions of a capacity. Constraints and demand charges are examples. These are expected to be very non-linear, but they will nonetheless be represented by a capacity cost and the capacity to which they apply. This function may be trivial or empty at transactive nodes where no capacity costs or incentives are to be included in the output TIS.

The output from this sub-function is the sum of products of pairs of capacity costs C_(C,b,n) (units: cost per power capacity) and average power capacity {circumflex over (P)}_(C,b,n) (units: average power) for each respective IST interval n.

$\begin{matrix} {\sum\limits_{b = 1}^{B}\; {C_{C,b,n} \cdot {\hat{P}}_{C,b,n}}} & \left( {{Sub}\text{-}{Function}\mspace{14mu} 3.4} \right) \end{matrix}$

3.5 Calculate Total Infrastructure Cost/Incentive—for each IST interval, sum the infrastructure (e.g., time-based) costs that should be applied during the interval. This function may be trivial or empty at transactive nodes where no infrastructure costs or incentives are to be included in the output TIS.

The output from this sub-function is the sum of products of pairs of infrastructure costs C_(I,c,n) (units: cost per time) and the respective interval duration Δt_(n) (units: time).

$\begin{matrix} {\sum\limits_{c = 1}^{C}\; {{C_{I,c,{.n}} \cdot \Delta}\; t_{n}}} & \left( {{Sub}\text{-}{Function}\mspace{14mu} 3.5} \right) \end{matrix}$

3.6 Calculate Total Other Cost/Incentive—for each IST interval, sum those influences that cannot be described by the energy, capacity, and infrastructure functions. (Other Cost/Incentive functions are desirably used infrequently for influences that cannot be described with the other functions. The representation of cost by this function should still be a defensible cost of delivered energy and will be subject to comparison against other cost accountings over relatively long time periods.) This function may be trivial or empty at transactive nodes where no other costs or incentives are to be included in the Output TIS.

The output from this sub-function is the sum of “Other” costs C_(O,d,n) (units: cost).

$\begin{matrix} {\sum\limits_{d = 1}^{D}\; C_{O,d,n}} & \left( {{Sub}\text{-}{Function}\mspace{14mu} 3.6} \right) \end{matrix}$

3.7 Calculate Output TIS—a simple parametric function that combines outputs from above functions to complete calculation of the Output TIS for this transactive node. The sums completed by five other sub-functions appear in this sub-function. Details about this function are expanded upon in the Section 3.7 Details about the Calculate Output TIS Function.

$\begin{matrix} {{TIS}_{n} = \frac{\begin{matrix} {{\sum\limits_{a = 1}^{A}\; {{C_{E,a,n} \cdot {\hat{P}}_{G,a,n} \cdot \Delta}\; t_{n}}} + {\sum\limits_{b = 1}^{B}\; {C_{C,b,n} \cdot {\hat{P}}_{C,b,n}}} +} \\ {{\sum\limits_{c = 1}^{C}\; {{C_{I,c,{.n}} \cdot \Delta}\; t_{n}}} + {\sum\limits_{d = 1}^{D}\; C_{O,d,n}}} \end{matrix}}{\sum\limits_{a = 1}^{A}\; {{{\hat{P}}_{G,a,n} \cdot \Delta}\; t_{n}}}} & \left( {{Sub}\text{-}{Function}\mspace{14mu} 3.7} \right) \end{matrix}$

3.8 Calibrate/Normalize TIS—algorithm by which the output TIS are to be compared against and perhaps made to track other cost accounting methods. If the calculation of a TIS is meaningful as the delivered cost of electrical energy, it should track other reasonable accountings of the delivered cost of electrical energy over relatively long periods of time. In some embodiments, this is a general requirement on the TIS. This general requirement may be enforced by a bias input that will force the TIS to track other less dynamic accountings and thereby correct the TIS.

3.9 Interpolate Intervals Service Functions—parse energy and costs from coarse intervals into multiple sub-intervals. This function is necessary because the set of IST intervals to be used by the output TIS will have divided some prior intervals into sub-intervals. This function is a service function that is called as often as it is desired. The objects TIS and TFS may simply be replicated for each sub-interval. (While many complex methods may evolve to interpolate and assign costs and average power to sub-intervals, in certain embodiments of the disclosed technology, the cost and average power from an interval are assigned to its sub-intervals.)

Inputs:

-   -   Energy cost, scheduled/committed non-transactive energy pairings         for each non-transactive generation or import resource at a time         interval         -   (IST*_(n), Δt*_(n), (C_(E,1,n), {circumflex over             (P)}_(G,1,n)), (C_(E,2,n), {circumflex over (P)}_(G,2,n)), .             . . , (C_(E,a,n), {circumflex over (P)}_(G,a,n)), . . . ,             (C_(E,A,n), {circumflex over (P)}_(G,A,n))),             where n is a time interval of the TIS numbered from 0 to 55;             IST is interval start time n in a series of interval start             times; Δt_(n) is the duration of interval n; C_(E,a,n) is             the energy cost term (e.g., units $/kWh, like the TIS) of             the scheduled generation or import resource a for IST             interval n, and {circumflex over (P)}_(G,a,n) is the average             generated or imported power from generation or import             resource a during time interval n. Its units are the same as             for TFS (e.g., average power). (The asterisk indicates that             this series of Interval Start Times and durations will             likely differ from those that have been calculated to be             used with the Output TIS and Output TFS. The function 3.10             Interpolate Intervals Service will sort this out for the             inputs into the other sub-functions. See, e.g., Figure C-4.)     -   Input TIS and input TFS pairings from each transactive node         neighbor for each time interval         -   (IST*_(n), Δt*_(n), (TIS_(1,n), TFS_(1,n)), (TIS_(2,n),             TFS_(2,n)), . . . , (TIS_(j,n), TFS_(j,n)), . . . ,             (TIS_(J,n), TFS_(J,n))),             where TIS_(j,n) and TFS_(J,n) are the input transactive             signals from transactive node neighbor j during time             interval n. This input should be considered a special case             of the input described in the preceding bullet. (At times             that energy is predicted to be imported from a transactive             neighbor, the corresponding TIS and TFS are special cases of             C_(E,a,n) and P_(G,a,n) and will be treated very much the             same.)     -   Interval start time series         -   {IST₀, IST₁, . . . , IST_(N)}     -   and interval duration series         -   {Δt₀, Δt₁, . . . , Δt_(N)}             to be used for Output TIS and Output TFS. (These notations             do not have asterisks because they are final intervals to be             used in the output transactive signals after this             iteration.) In FIG. 4, the Interval Start Time Series is             shown as an input to the function 3.10 Interpolate Intervals             Service, which have the responsibility to resolve any             discrepancies between various representations of intervals.     -   Energy term(s) C_(E) from applicable incentive toolkit         functions, if any. (Energy terms C_(E) have the same usage and         interpretation regardless of whether they are used inside or         outside a Toolkit Incentive Function. This term accounts for         costs that are roughly proportional to an amount of energy that         is being generated or imported into a transactive node's         boundary.) The format should be identical to that stated above         for non-transactive energy pairings.     -   Average Power terms(s) {circumflex over (P)}_(G) from applicable         incentive toolkit functions, if any. (The average power terms         are used similarly regardless of whether they are used in or         outside a Toolkit Incentive Function. These terms are an         accounting of the average power that is either generated within         our imported into a transactive node boundary.) The format         should be identical to that stated above for non-transactive         energy pairings.     -   Capacity term(s) C_(C) from applicable Incentive toolkit         functions, if any, applicable at each IST interval         -   (IST*_(n), Δt*_(n), (C_(C,1,n), {circumflex over             (P)}_(C,1,n)), (C_(C,2,n), {circumflex over (P)}_(C,2,n)), .             . . , (C_(C,b,n), {circumflex over (P)}_(C,b,n)), . . . ,             (C_(C,B,n), {circumflex over (P)}_(C,B,n))),             where C_(C,b,n) is the cost to be applied to capacity cost             item b paired with the capacity {circumflex over             (P)}_(C,b,n) to which it applies, and {circumflex over             (P)}_(C,b,n) is the average power capacity for capacity cost             item b to be multiplied by capacity cost C_(C,b,n) for IST             interval n.     -   Infrastructure term(s) C_(I) from applicable incentive toolkit         functions, if any         -   (IST*_(n), Δt*_(n), C_(I,1,n), C_(I,2,n), . . . , C_(I,c,n),             . . . , C_(I,C,n)),             where C_(I,c,n) is the infrastructure term c for the IST             interval n.     -   Other term(s) C_(O) from applicable Incentive toolkit functions,         if any, for each IST interval         -   (IST*_(n), Δt*_(n), C_(O,1,n), C_(O,2,n), . . . , C_(O,d,n),             . . . , C_(I,D,n)),             where C_(O,d,n) is the “Other” influence term d for IST             interval n.     -   Exemplary alternative cost accounting(s) for use by function 3.9         Calibrate/Normalize TIS. Examples include wholesale energy costs         for the same energy or utility expenses.

Interim Calculation Products:

-   -   Total Cost of Non-transactive Energy Imports     -   Total Cost of Non-transactive Energy Generation     -   Total Cost of Energy Imported from Transactive neighbors     -   Total Capacity Cost/Incentive     -   Total Infrastructure Cost/Incentive     -   Total Other Cost/Incentive     -   Total Cost     -   Total Energy Imported or Generated     -   Additionally, interim calculations may be used to represent         prior interval information in terms of the new IST time series         and interval durations that are to be used by the Output TIS.

Outputs:

-   -   New “Updated” Output TIS at this transactive node.

Function/Process: Each of the sub-functions/sub-processes should be defined, but sub-function 3.8 Calculate Output TIS defines the parametric calculation of the output TIS from the energy, capacity, infrastructure, and other parameters and how the parameters are to be applied. The implementer who understands sub-function 3.8 Calculate Output TIS will have the insight to formulate toolkit functions and will have considerable flexibility in the way such toolkit functions are formulated.

Dependencies:

-   -   Uses input of new IST time series from process 2. Calculate New         Transactive Signal Intervals     -   Uses input of TIS and TFS from at least one transactive neighbor         via process 1. Receive Transactive Signals     -   Process inputs may come from Calculate Applicable Toolkit         Incentive Functions     -   Process inputs may come from Resource Schedules and Cost Buffer.     -   Output TIS from this process is used by process 7. Send         Transactive Signals.     -   Output TIS from this process may be used by Calculate Applicable         Toolkit Response Functions dP(TIS,OLC) if this transactive node         owns responsive assets.

Notes:

-   -   Each transactive node produces one and only one TIS for itself         for each 5-minute update iteration.     -   The TIS itself is a time series that expresses the delivered         cost of energy into the future about 3 days, or so, as is         defined by the IST time series.

FIG. 38 is a flowchart 3800 illustrating an exemplary formulate TIS process.

Details about the Function 3.7 Calculate Output TIS

Purpose: Describes the final parametric calculation of the output TIS. This sub-function consists of a simply stated function of the sum products of other sub-functions 3.2 through 3.7. This sub-function creates a level of standardization that will help ensure that the TIS at distributed points in a transactive control and coordination system are defensible representations of the “delivered cost of energy.”

Applicability: A sub-function of 3. Formulate TIS Process that should be calculated at the update frequency at transactive nodes.

Sub-Functions and Sub-processes: None. This is a simple arithmetic function of sums that have been calculated by sub-functions 3.2 through 3.7.

Inputs:

-   -   Summed cost of energy terms

$\begin{matrix} {\sum\limits_{a = 1}^{A}\; {{C_{E,a,n} \cdot {\hat{P}}_{G,a,n} \cdot \Delta}\; t_{n}}} & \left( {{Sub}\text{-}{Functions}\mspace{14mu} 3.2\mspace{20mu} {and}\mspace{14mu} 3.3} \right) \end{matrix}$

from sub-functions 3.2 Calculate Total Cost of Non-Transactive Energy Generation and Imports and 3.3 Calculate Total Cost of Energy Imported from Transactive nodes

-   -   Summed cost of capacity terms

$\begin{matrix} {\sum\limits_{b = 1}^{B}\; {C_{C,b,n} \cdot {\hat{P}}_{C,b,n}}} & \left( {{Sub}\text{-}{Function}\mspace{14mu} 3.4} \right) \end{matrix}$

from sub-function 3.4 Calculate Total Capacity Cost/Incentive

-   -   Summed cost of infrastructure terms

$\begin{matrix} {\sum\limits_{c = 1}^{C}\; {{C_{I,c,{.n}} \cdot \Delta}\; t_{n}}} & \left( {{Sub}\text{-}{Function}\mspace{14mu} 3.5} \right) \end{matrix}$

from sub-function 3.5 Calculate Total Infrastructure Cost/Incentive

-   -   Summed other costs

$\begin{matrix} {\sum\limits_{d = 1}^{D}\; C_{O,d,n}} & \left( {{Sub}\text{-}{Function}\mspace{14mu} 3.6} \right) \end{matrix}$

from sub-function 3.6 Calculate Total Other Cost/Incentive

-   -   Summed energy

$\begin{matrix} {\sum\limits_{a = 1}^{A}\; {{{\hat{P}}_{G,a,n} \cdot \Delta}\; t_{n}}} & \left( {{Function}\mspace{14mu} 10} \right) \end{matrix}$

that is predicted to be imported and/or generated at this transactive node as has been calculated in function 10. Sum Total Predicted Resource.

Outputs:

-   -   One current output TIS time series for this transactive node

Function/Process:

This sub-function simply adds the individual cost summations from sub-functions 3.2, 3.3, 3.4, 3.5, and 3.6 and divides that sum by the total energy that is imported into or generated within the boundaries of this transactive node as was summed by sub-function 3.7:

$\begin{matrix} {{\frac{\begin{matrix} {{\sum\limits_{a = 1}^{A}\; {{C_{E,a,n} \cdot {\hat{P}}_{G,a,n} \cdot \Delta}\; t_{n}}} + {\sum\limits_{b = 1}^{B}\; {C_{C,b,n} \cdot {\hat{P}}_{C,b,n}}} +} \\ {{\sum\limits_{c = 1}^{C}\; {{C_{I,c,{.n}} \cdot \Delta}\; t_{n}}} + {\sum\limits_{d = 1}^{D}\; C_{O,d,n}}} \end{matrix}}{\sum\limits_{a = 1}^{A}\; {{{\hat{P}}_{G,a,n} \cdot \Delta}\; t_{n}}},{or}}{{TIS} = \frac{\begin{matrix} {{{energy}\mspace{14mu} {cost}} + {{capacity}\mspace{14mu} {cost}} +} \\ {{{infrastructure}\mspace{14mu} {cost}} + {{other}\mspace{14mu} {costs}}} \end{matrix}}{Energy}}} & \left( {{Sub}\text{-}{Function}\mspace{14mu} 3.7} \right) \end{matrix}$

The function shown above for interval n should be performed for all intervals that are to be used by the Demonstration for its transactive signals.

Dependencies:

-   -   Will use sub-function 3.10 Interpolate Intervals Service         Functions to convert intervals of inputs into those of the         updated IST time series that is to be used by the output TIS.     -   The output TIS produced by this sub-function is one of the two         transactive signals that function 7. Send Transactive Signals         will act upon and send.

Notes:

-   -   This function assumes that intervals have been aligned and         modified to be consistent with the new IST intervals that were         determined by process 2. Calculate New Transactive Signal         Intervals. If that is not the case, the sub-function 3.10         Interpolate Intervals Service Functions should be applied until         inputs to this sub-function have been stated in terms of the IST         intervals for which the output TIS will be produced.     -   If properly formulated, the units of TIS will be cost per         energy. Dimensional unit analysis is a candidate component for         conformance testing to be performed on any implementation that         follows this toolkit framework.

4. Formulate TFS

Purpose: Formulate one current transactive feedback signal (TFS) for the electrical interface between this transactive node and each of its transactive neighbors.

Applicability: This process should be completed at the update frequency by transactive nodes.

Sub-Functions and Sub-processes:

4.1 Interpolate Intervals Service Functions—function, or set of functions, by which the inputs to this process may be restated using the current interval start time (IST) series. If input time series are found to use dated time intervals or any other representation of future intervals other than the current IST series, this function should be called until the dissimilarities are resolved. This function should also be called early during an update interval iteration to create updated, default versions of a recent prior transactive feedback signals (TFS) that may be used if, for any reason, this transactive node fails to formulate a TFS by the time it is used.

4.2 Predict Net Resource Surplus or Shortage—take the difference between total resource from A resources and total load supplied by this transactive node to determine the net surplus or shortage for each future interval n. The net surplus or shortage is the average power over an interval that should be sent to or received from transactive neighbors during that interval—an imbalance anticipated to occur at this transactive node. Therefore, the net surplus or shortage should equal the sum of all changes to the TFS for each interval at this transactive node.

$\begin{matrix} {{\sum\limits_{\;}^{\;}\; {TFS}_{n}} = {{\sum\limits_{a = 1}^{A}\; {\hat{P}}_{G,a,n}} - {\sum{\hat{L}}_{n}}}} & \left( {{Sub}\text{-}{Function}\mspace{14mu} 4.2} \right) \end{matrix}$

Total average load at each interval Σ{circumflex over (L)}_(n) is a calculated input that should be retrievable from the Predicted Inelastic and Elastic Load Buffer. The total resource

$\sum\limits_{a = 1}^{A}\; {\hat{P}}_{G,a,n}$

is a calculation available from the Total Predicted Resource Buffer, a product of 10. Sum Total Predicted Resource. (Desirably, there is a connection between this calculated imbalance and resource planning.)

4.3 Disaggregate Net Resource Surplus or Shortage—allocate the net resource surplus or shortage among this transactive node's transactive neighbors by formulating or modifying the TFS for each such interface. The newly formatted TFS are then stored into the Output TFS Buffer.

Today, this prediction would rely on centralized power-flow solvers. In a fully distributed system, however, new prediction tools can be used.

This transactive node object should supply to this sub-function the current list of transactive neighbors for which TFS should be calculated. It may also provide simple ratios or detailed topological information that can be used eventually to predict load flow between this transactive node and its transactive neighbors, e.g., TFS series. Current information about the transactive node object is assumed to be available from a Node State and Status Buffer.

4.4 Refresh Default Output TFS—early during each IST update interval, this process should refresh the last calculated versions of TFS found in the Output TFS Buffer and restate them using the current IST series. Thereafter, the restated, refreshed TFS may be returned to the buffer and used as default values if, for any reason, this transactive node should fail to formulate the current TFS by the time they are used.

Inputs:

-   -   Predicted total load supplied Σ{circumflex over (L)}_(n) at each         future interval n of the current IST series from the Predicted         Inelastic and Elastic Load Buffer     -   Predicted total resource

$\sum\limits_{a = 1}^{A}\; {\hat{P}}_{G,a,n}$

at each future interval n of the current IST series.

(This is now calculated by a sub-function of this process, but it can be made available from a common buffer of the toolkit framework.) This input should be available from the Total Predicted Resource Buffer.

-   -   Information from this transactive node object concerning its         transactive neighbors that should expect to receive a TFS from         this transactive node, available from the Node State and Status         Buffer.     -   Information from this transactive node's object that will be         used to allocate, or disaggregate, the net surplus or shortage         among the TFS that are to be stated form each transactive         neighbor, available from the Node State and Status Buffer.     -   The current IST series available from the Current IST Series         Buffer.

Outputs:

-   -   One output TFS for each transactive neighbor stored into and         available from the Output TFS Buffer.

Function/Process: Refer to the descriptions of the sub-functions above as the sub-functions were being introduced.

Dependencies:

-   -   This process formulates one of two transactive signal types that         should be available from the Output TFS Buffer to be conveyed by         this transactive node to its transactive neighbors at the update         frequency by 7. Send Transactive Signals.     -   This process expects that the current IST series will have been         created by 2. Calculate New Transactive Signal Intervals and         available from the Current IST Series Buffer.     -   This process expects that the current sum total load will have         been calculated by function 5. Sum Total Predicted Load and         available from the Predicted Inelastic and Elastic Load Buffer.     -   This process expects that the total predicted resource will have         been calculated by function 10. Sum total Predicted Resource.

Notes:

-   -   The TFS is indeed a feedback signal, but the transactive control         and coordination system is not a closed-loop feedback control         system in the classical sense. First, the magnitude of resource         from responsive asset systems is too small for us to expect         closed-loop control. Second, the TIS is decidedly grounded as a         meaningful delivered cost of energy, not free to represent large         incentive swings as could a local marginal price. There is weak         or no integral control in the system.     -   The transactive feedback signal (TFS) may not be as dynamic and         useful as the transactive incentive signal (TIS) will be. The         TFS will be affected by a relatively small fraction of         responsive asset systems at places throughout the transactive         control and coordination system. Transmission and generation         entities are unengaged by the project's scale and are therefore         unresponsive to changes that will be observed in the TFS.

FIG. 39 is a flowchart 3900 of an exemplary formulate TFS process

5. Sum Total Predicted Load

Purpose: Process to add the total inelastic (non-transactive) and elastic (transactive) electrical load components being supplied within the boundaries of this transactive node. (In the illustrated embodiment, electrical energy that is to be exported outside the boundaries of a transactive node is not part of this sum.)

Applicability: This function applies to transactive nodes and should be updated at the update frequency; however, this process becomes trivial for transactive nodes that supply no elastic electric load, no inelastic electric load, or neither elastic nor inelastic electric load within the boundaries of the transactive node.

Sub-Functions and Sub-Processes:

5.1 Interpolate Intervals Service Functions—suite of functions that may be called upon should any inputs to this function note yet exist using the current set of interval start times that should be available from the Current IST Series Buffer.

5.2 Sum Inelastic Load—sums the entries in the Inelastic Load Prediction Buffer that are relevant to the current update interval iteration.

The Inelastic Load Prediction Buffer may (or may not) have a multiplicity of relevant entries that should be summed. For example the buffer might possess a bulk load prediction that is simply based on historical trends over the past week, the inelastic prediction for a large water heater responsive asset system, and the inelastic prediction for a voltage-response asset. (In certain embodiments, care should be taken not to double count any of the load as this sum is taken.) For each of this component addends k, the buffer should possess a relatively current entry L_(inelastic,k). Each entry should state average load (unit: average power) to be consumed (or generated) by it during each of a series of intervals.

If an entry from the buffer is found to have intervals other than those in the current IST series, function 5.1 Interpolate Interval Service Functions should be called upon to resolve the discrepancy and restate the entry contents using the current IST interval set.

Ideally, all current, relevant contents of the buffer will be evident from the entries' interval start time IST₀ time. Preferably, the buffer contents that are to found and summed by this sub-function for each iteration should be attributes of this transactive node, knowable from the contents of the Node State and Status Buffer.

The output product of this sub-function is a single time series ΣL_(inelastic,n) that has summed components k.

5.3 Sum Change in Elastic Load—sums the entries in the Toolkit Response Function Output Buffer that are relevant to the current update interval iteration. If toolkit functions have been employed for responsive asset systems at this transactive node, one or more entries will be found in the buffer to be summed in this sub-function. Note that only the change in elastic load is to be found in the buffer and summed for each interval start time interval by this sub-function. For each of this component addends j, the buffer should possess a relatively current entry ΔL_(elastic,j.) Each entry should state the change in average load (unit: average power) it predicted to be consumed (or generated) by it during each of a series of intervals.

If an entry from the buffer is found to have intervals other than those in the current IST series, function 5.1 Interpolate Interval Service Functions should be called upon to resolve the discrepancy and restate the entry contents using the current IST interval set.

As was the case for sub-function 5.3 above, the contents of the buffer that are to found and summed by this sub-function for each iteration should be an attribute of this transactive node, knowable from the contents of the Node State and Status Buffer.

The output product from this sub-function is a single time series ΣΔL_(elastic,n) that has summed components j.

5.4 Sum Total Inelastic and Change in Elastic Load—function by which total inelastic load predictions and predicted changes in elastic load are finally summed to calculate a total to be placed into the Predicted Total Inelastic and Elastic Load Buffer. This function completes the simple arithmetic sum

ΣL _(total,n) =ΣL _(inelastic,n) +ΣΔL _(elastic,n),  (Function 5.)

where ΣL_(total,n) is the sum of total inelastic load ΣL_(inelastic,n) and total change in elastic load ΣΔL_(elastic, n) for IST interval n at this transactive node.

5.5 Refresh Predicted Total Inelastic and Elastic Load—succeeding calculations will expect that the predicted total inelastic and elastic load will be available according to current IST intervals. Therefore, early in each update interval interation, the most current representation of that sum should be located within the Predicted Total Inelastic and Elastic Load Buffer and subjected to function 5.1 Interpolate Intervals Service Functions to recast the buffer contents into a default buffer entry that uses the current set of interval start times (IST). If for any reason this transactive node fails to later update its prediction of the sum into the buffer, the default value may be used instead.

Inputs:

-   -   Set of predicted inelastic load {L_(inelastic,1),         L_(inelastic,2), . . . , L_(inelastic,k), . . . ,         L_(inelastic, K)} for each of the K components of total         inelastic load, each of which predicts average load (units:         average power) for interval start time interval n. This set of         entries should be found from within the Inelastic Load         Prediction Buffer.     -   Set of predicted changes to elastic load {ΔL_(inelastic,1),         ΔL_(inelastic,2), . . . , ΔL_(inelastic,j), . . . ,         ΔL_(inelastic, J)} for each of the J components of total change         in elastic load, each of which predicts change in average load         (units: average power) for interval start time interval n. This         set of entries should be found from within the Toolkit Response         Function Output Buffer.     -   Current interval start time (IST) series from the Current IST         Series Buffer     -   List of those members of the Inelastic Load Prediction Buffer,         if any, which are expected to be found and used by this process,         which list should be obtained from attributes of this         transactive node found in the Node State and Status Buffer.     -   List of those members of the Toolkit Response Function Output         Buffer, if any, which are expected to be found and used by this         process, which list should be obtained from attributes of this         transactive node found in the Node State and Status Buffer.

Outputs:

-   -   Total predicted load L_(total, n) for each of the current IST         intervals to be stored into the Predicted Inelastic and Elastic         Load Buffer.

Function/Process: The steps of this process were stated above with the introductions of sub-functions. Overall, the process completes the simple arithmetic sum

ΣL _(total,n) =ΣL _(inelastic,n) +ΣΔL _(elastic,n),  (Function 5)

where ΣL_(total,n) is the sum of total inelastic load ΣL_(inelastic,n) and total change in elastic load ΣΔL_(elastic, n) for IST interval n at this transactive node.

Dependencies:

-   -   Should call upon current inelastic load predictions from the         Inelastic Load Prediction Buffer having been updated frequently         by process 6. Predict Applicable Inelastic Load using         Trends/Models.     -   For those transactive nodes that have responsive asset systems         and therefore employ toolkit functions, this function expects         that current predictions of changes in elastic load are         available from the Toolkit Response Function Output Parameter         Buffer having been updated frequently by process Calculate         Applicable Toolkit Response Function(s).     -   The output from this function is an input to process 4.         Formulate TFS.

Notes:

-   -   If the prediction of current total elastic and inelastic load         components cannot be calculated promptly by the time they are         used by the transactive node, prior calculations from the         Predicted Inelastic and Elastic Load Buffer should be used by         process 4. Formulation TFS.     -   It would be ideal if inputs into and outputs from this function         were properly formatted using the current interval start time         series (IST) that should exist in the Current IST Series Buffer.         Keeping the current outputs of functions and processes aligned         with the current IST series will greatly simplify later         successive calculations. If that cannot be accomplished,         interpolation service functions should be called upon.     -   Implementers might choose to have this process additionally         interact with the system management layer. If, for example, this         transactive node fails to update its load predictions and         therefore uses default, buffered estimates, such events might be         counted and/or flagged to initiate notifications or alerts. Such         a capability would be nice to have, but it is probably not an         essential part of the toolkit framework. System management for         this process would serve business entities that are relevant to         the “generic” system implementation.

FIG. 40 is a flowchart 4000 of an exemplary sum total predicted load process.

6. Calculate Applicable Toolkit Load Functions

Purpose: This process block represents from zero to many specific toolkit library functions that may be incorporated into the toolkit framework here. The toolkit functions that become instantiated at this location should represent and predict elastic and inelastic loads and should result in a reasonably complete and accurate prediction of the entire load that is supplied within the boundaries of this transactive node during each IST interval.

Most generally, these toolkit functions may be characterized by their inputs and outputs and by their generalized functional responsibilities within the toolkit framework. A template is developed for the specification of toolkit functions (see SubAppendix B). Owners of transactive nodes, who represent the unique perspective under which this transactive node should be managed, should select and/or help create specific toolkit function(s) that model the responsive asset systems and inelastic loads that they have or plan to implement. See Table 25 for an example list of toolkit load functions.

Modular toolkit functions may be implemented and shared via combinations of their functional descriptions, pseudo code implementations, and reference code, all of which are recommended components of the recommended toolkit function template.

The location of this block within the toolkit framework is intended for toolkit functions that predict the behaviors of two different types of loads:

-   -   responsive asset system—an elastic load m for which its toolkit         function predicts both its inelastic load component L_(m) and a         change in elastic load ΔL_(m) using the current output TIS and         often other local conditions as inputs.     -   inelastic load—other inelastic load component for which its         toolkit function predicts only its inelastic load component         L_(m).

Of interest are those responsive asset systems that can be applied to the transactive control and coordination system. (In certain embodiments, responsive asset systems have been defined to be applied within reliability or conservation and efficiency test cases as well. Not all responsive asset systems are being used in the transactive control and coordination system test cases.) A toolkit function should be defined for each unique implementation of each major type of responsive asset system. Each toolkit function should first calculate the inelastic load L_(m), which predicts when and how much energy the responsive asset system would consume if it were not influenced by the output TIS. The prediction of inelastic load component is placed into the Inelastic Load Prediction Buffer. The toolkit function should then predict the change in elastic load ΔL_(m) that is caused by the condition of the output TIS. The prediction of elastic load component is placed into the Elastic Load Prediction Buffer. It is acceptable that the elastic load components may be zero during intervals when the responsive asset system is not predicted to be engaged by the output TIS.

Another output from a toolkit function should be a representation of the planned control action by which the responsive asset system will be induced to change its energy consumption in light of the state of the output TIS for each interval. For example, some responsive asset systems may be either active or curtailed (e.g., populations of water heaters), in which case a binary indicator might be used for each interval. Other systems are able to enter any of multiple discrete levels of response (e.g., GE smart appliances), in which case one of several discrete levels should be specified for each interval. Still other systems may provide a continuum of possible responses and use a representation of percentage. (An interesting example of this continuum of responses will occur where customers are provided a means to view the output TIS itself on an in-home display and respond correspondingly with a continuum of behavioral responses.) Eventually, as time marches toward the interval of interest and the interval becomes that of IST₀, the responsive asset system should be expected to take the predicted, prescribed action. The implementations of responsive asset systems will be diverse, but it is in the representation of these predicted, planned control actions where standardization may be particularly useful.

An example would probably be useful concerning the portion of predicted load that should be included in this process from elastic loads, including responsive asset systems. Electrical consumption by a set of electric water heaters may be predicted quite well from measured trends and models of the water heaters and their owners' behaviors. The input information or parameters that influence such trends and models might include time of day, day of week, occupancy, outdoor temperature, and average outdoor temperature, for examples. In the toolkit framework, these pieces of information or parameters are referred to as other local conditions that should be available inputs if the transactive node is to accurately predict the load consumed by the water heaters. These predictions are to be completed within this process 6. Predict Applicable Toolkit Load Functions. The predicted load should be recorded for each such system in the Inelastic Load Prediction Buffer. If upon receipt of the current output TIS the water heaters would reduce their load, the change (e.g., only the change) would be predicted in a parallel calculation path and would be stored into the Elastic Load Prediction Buffer.

Toolkit functions can used to describe behaviors of individual devices. But the responsive asset systems of the Demonstration are primarily used for populations of devices. It is the statistical behavior of the populations, not individual devices that should be predicted.

Inelastic load components are similarly incorporated via their toolkit functions; however, no elastic load component should be created by these functions. Candidate inelastic load predictions might include feeders of residential customers, where the load of the population could be predicted from the time of day, average home square footage, average house age, outdoor temperature, and perhaps still other local conditions.

Regardless of whether a given toolkit function describes an elastic or an inelastic load, a load should never appear on both the resource and load sides of the toolkit framework formulation for any single interval n. Responsive asset systems may be either electrical loads or resources. Regardless, the toolkit functions whose influence is to be inserted at this location will affect the formulation of the TFS but will not directly influence the formulation of the output TIS. Responsive asset systems that should affect the delivered cost of energy (e.g., the TIS) at this transactive node should be inserted at location 8. Calculate Applicable Toolkit Resource Functions instead.

Using the above-stated criterion, the average power from a customer's renewable generator should probably be treated as a “negative” load (e.g., its toolkit function should be incorporated here) if it will never result in net metering. But if the utility at any time pays the customer net-metering payments for surplus energy that is produced by the resource, the resource should be included instead among resources, not loads, so that the net-metering charges may influence the formulation of the TIS (e.g., a toolkit function should be included for this system in the process 8. Calculate Applicable Toolkit Resource Functions).

Using the same reasoning, the present process should not predict bulk generation resources that are scheduled at this transactive node because costs should almost certainly be applied to the energy from such bulk resources.

The influences of elastic and inelastic load components should never be double counted. The influence of a load should appear only once if an accurate prediction of total load is to be formulated by this transactive node.

Toolkit functions may include learning algorithms and other means to improve the accuracy of their load predictions over time, but such complexities should be weighed against the Demonstration's desire to create and teach and implement these toolkit functions with its participants and within a tight development schedule.

See Table 25 for a list of example toolkit load functions.

Applicability: Any toolkit functions to be called upon in this process block should be called at the update frequency. It is conceivable but unlikely that a transactive node may have neither inelastic nor elastic load components that necessitate any toolkit functions be called within this process block.

Sub-Functions and Sub-Processes:

6.1 Interpolate Intervals Service Functions—a suite of service functions that may be called upon as they are desired to restate dated time series in terms of the current IST intervals. (These functions might be defined and used throughout the entire toolkit framework instead of uniquely defined for each process, as has been shown here.)

6.2m Toolkit Load Function—from zero to many individual toolkit functions from a toolkit function library that predict inelastic load and change in elastic load for each interval of the current IST series. Enough such toolkit functions should be incorporated and called upon to predict the entire load at this transactive node. Individual toolkit functions may be created or selected from a toolkit function library predict the behaviors of a responsive asset system; the behaviors of a group of inelastic loads; generation from small distributed generation resources that do not directly influence the formulation of the TIS; or large nebulous groups of ill-defined loads that can only be characterized by their historical trends.

It should be assumed that the list of M relevant toolkit functions are identified and known by this transactive node object and is available from the Node State and Status Buffer. Furthermore, the buffer should identify the sets of other local conditions inputs expected to be available to the M toolkit functions from the Toolkit Load Function Input Buffer.

A toolkit function should output its prediction of inelastic load into the Inelastic Load Prediction Buffer for the load being described and for a current IST interval. (The inputs expected by toolkit functions will be varied and may be dynamic.) If the function models and helps control responsive, elastic loads, the function should also create and output the planned control for the responsive load. A standardized advisory control signal to be sent to the responsive asset systems has been formulated and is available in SubAppendix C.

6.3 Refresh Predicted Inelastic Elastic Loads—early each update interval iteration, the most current contents of the Inelastic Load Prediction Buffer and Elastic Load Prediction Buffer should be retrieved by this sub-function and restated using 6.1 Interpolate Intervals Service Functions in terms of the current IST interval set. These updated buffer contents are then available to be used by default should this transactive node fail for any reason to calculate its load for the current iteration.

Inputs:

-   -   current IST interval series that is available from the Current         IST Series Buffer     -   current output TIS (units of “value” attribute: cost per energy)         from the Output TIS Buffer     -   other local conditions (OLC) (units: various) as might be         prescribed by specific toolkit functions and available from the         Toolkit Load Function Input Buffer     -   list of M toolkit functions that should be called at this         transactive node and the list of other local conditions data         inputs that will be used by the toolkit functions as should be         known by this transactive node object and available from the         Node State and Status Buffer.

Outputs:

-   -   Inelastic load predictions L_(m) (units: average power) for each         IST interval stored into the Inelastic Load Prediction Buffer     -   Elastic load predictions ΔL_(m) (units: change in average power)         for each IST interval stored into the Elastic Load Prediction         Buffer     -   Predicted control actions for responsive asset systems for each         IST interval stored into the Elastic Load Prediction Buffer.         (recommendation for units: {allowed: “0”; curtailed: “−1”};         {generation level L: “L”; . . . , generation level 2: “2”;         generation level 1: “1”; off: “0”; load reduction level −1:         “−1”; load reduction level −2: “−2”; . . . }; {continuum from         full generation: “100”; off: “0”; full load reduction: “−100})

Function/Process: Sub-functions 6.1 and 6.3 were described as they were being introduced in the text above. This document has stated functional responsibilities and an input/output model for the multiplicity of toolkit functions 6.2m Toolkit Load Function that are to be called upon during this process. Each toolkit function should use the provided template and should describe for itself what it is meant to accomplish within the functional responsibilities, inputs, and outputs that have been generally described here.

Dependencies:

-   -   The current TIS should have been calculated by 3. Formulate TIS         and available from the Output TIS Buffer.     -   Various current other local conditions should be available from         the Toolkit Load Function Input Buffer. The list of relevant         other local conditions should be known to the transactive node         object and available from the Node State and Status Buffer. Note         that the other local conditions might themselves use management         of other data collection and maintenance systems and processes.     -   A list of functions 6.2m Toolkit Load Functions should be         unambiguously named and known to this transactive node object         available from the Node State and Status Buffer.     -   Process 2. Calculate New Transactive Signal Intervals should         have run recently to provide to this process current IST         intervals available from the Current IST Series Buffer.     -   This process inserts up to M entries into each the Inelastic         Load Prediction Buffer and Elastic Load Prediction Buffer, one         for each toolkit function that is called. The contents of these         buffers should be current and available to be summed by         process 5. Sum Total Predicted Load.

Notes:

-   -   No load or resource should appear on both the load and resource         sides of the toolkit formulation for any given IST interval.     -   The sum of the inelastic load stored into the Inelastic Load         Prediction Buffer and change in elastic load stored into the         Toolkit Response Function Output Buffer for a give toolkit         function should closely predict the actual load, providing the         TIS and other local conditions (OLC) remain about the same until         the corresponding IST, interval becomes IST₀.     -   It is hoped but not required that model-based predictions of         both the inelastic-load and change in elastic-load components         may improve over time as more sophisticated toolkit functions         use historical feedback to improve their algorithms.     -   Implementers are encouraged to use this process and its toolkit         functions for model-based load predictions, regardless of         whether they describe elastic load.

TABLE 25 Example Resource, Incentive and Load Toolkit Functions Resource or Incentive Load 1.0 Imported Electrical Energy 1.0 Bulk Inelastic Load 1.1 Non-Transactive Imported 1.1 Bulk Commercial Load Energy 1.2 Bulk Industrial Load 1.2 Transactive Imported 1.3 Bulk Residential Load Energy 1.4 Small Wind Generator Negative Load 2.0 Renewable Energy 1.5 Small-Scale Distributed Generator Negative Load Resource 1.6 Small-Scale Solar Generator Negative Load 2.1 Wind Energy 2.0 General Event-Driven Demand Response 2.2 Solar Energy 2.1 Commercial Event-Driven Demand Response 2.3 Hydropower 2.2 Event Driven Distribution System Voltage Control 3.0 Fossil Generation 2.4 Residential Event-Driven Demand Response 4.0 General Infrastructure 2.5 Non-Renewable Distributed Generation Event-Driven Cost Demand Response 5.0 System Constraints 3.0 General Time-of-Use Demand Response 5.1 Transmission Flowgate 3.1 Battery Storage--Time-of-Use 5.2 Equipment and Line 3.2 Commercial Time-of-Use Demand Response Constraints 3.4 Residential Time-of-Use Demand Response 6.0 System Energy Losses 3.5 Time-of-Use Distribution System Voltage Control 6.1 Transmission Losses 3.6 Time-of-Use Electric Vehicle Charging 6.2. Distribution Losses 4.0 General Real-Time Continuum Demand Response 7.0 Demand Charges 4.1 Battery Storage--Real-Time 7.1 BPA Demand Charges 4.2 Commercial Real-Time Demand Response 8.0 Market Impacts 4.3 Real-Time Distribution System Voltage Control 8.1 Spot Market Impacts 4.5 Residential Real-Time Demand Response 5.0 General Manual or Behavioral Demand Response 5.1 Residential Behavioral Response to Portals or In-Home Displays 5.2 Residential Behavioral Response--No Portals or In- Home Display 5.3 Manual Commercial Demand Response 5.4 Manual Non-Renewable Distributed Energy Resources Demand Response

FIG. 40 is a flowchart 4000 of an exemplary “calculate applicable toolkit load functions” process.

7. Send Transactive Signals

Purpose: Method by which output transactive signals are conveyed from this transactive node to each one of its transactive neighbors. Most generally, there will be no single approach to completing this process because transactive is tied to no single communication technology, medium, or protocol. Transactive neighbor pairs should negotiate and agree upon these details. On the other hand, the Demonstration has elected to convey transactive signals almost exclusively via secure Internet.

Applicability: An process that should be completed at the update frequency by a transactive node.

Sub-Functions and Sub-processes: The following high-level responsibilities should be addressed, regardless of the platforms on which it is designed:

-   -   Format transactive signals according to published         recommendations, including published XML schema.     -   Coordinate timing with transactive node object states during         each update interval and iteration     -   Compare and coordinate Transactive neighbor list with         transactive node object state.

Inputs:

One output TIS series from process 3. Formulate TIS

-   -   One output TFS series for each transactive neighbor from         process 4. Formulate TFS

Outputs:

-   -   Paired couples of output TIS and output TFS sent to each         transactive neighbor.

Dependencies:

-   -   Receives current output TIS from Output TIS Buffer

Notes:

-   -   This process or function is trivial from a functional         perspective, but it is useful from a system interoperability         perspective. Transactive nodes that employ unlike software and         computational architectures should still be able to send and         receive these signals from their transactive neighbors.

This function or process is also useful from a cyber-security perspective. Both the senders and recipients of transactive signals should be satisfied that their systems will remain safe from attack.

FIG. 43 is a flowchart 4300 of an exemplary “send transactive signals” process.

8. Calculate Applicable Toolkit Resource and Incentive Functions

Purpose: A multiplicity of toolkit functions may be applied at this location within the toolkit framework to address resources and incentives. Toolkit functions should be created or selected from a toolkit library to represent the energy resources and incentives that are be applied at this transactive node during each IST interval. The costs that are calculated by the toolkit functions in turn may incentivize or disincentivize consumption and generation of electricity through their effects on the transactive incentive signal.

See Table 25 for a list of example toolkit resource and incentive functions. Refer to SubAppendix B for a template that may be used to specify additional toolkit resource and incentive functions as they are developed.

Applicability: A transactive node should calculate at least one toolkit function at the update frequency.

Sub-Functions and Sub-Processes:

8.1 Interpolate Intervals Service Functions—a suite of service functions that can accept stale, dated data and restate the data in terms of the current IST interval series. (These functions might be defined and used throughout the entire toolkit framework instead of uniquely defined for each process, as has been shown here.) 8.2 Refresh Predicted Resources and Incentives—Early during each update interval, this sub-function retrieves the most recent entries from the Resource Schedules and Cost Buffer and restates the records in terms of the current IST series. If for any reason this transactive node fails to complete the present process by the time its outputs are used, the restated records may be used as default records.

8.3 Assign Energy Cost and Average Power—a sub-function of a toolkit resource and incentive function in which cost C_(E,a,n) (units: cost per energy) is assigned to each component a of energy {circumflex over (P)}_(G,a,n) (units: average power) that is either imported into or generated within the boundaries of this transactive node. In particular embodiments, one responsibility of a toolkit resource and incentive function is to calculate and report one of each of these two quantities for each current IST interval n. Either of the calculated quantities may be zero. The calculated values will differ depending on selected toolkit function and the resource or effect that is being modeled by the selected toolkit function.

Example energy costs and energies that that should be captured using this sub-function include

-   -   The cost of energy from traditional bulk generation     -   The cost of energy from renewable energy resources like wind.         (Wind energy is desirably incentivized by applying its costs to         its infrastructure and not to the energy that is produced.         Thereby, it causes a downward influence on the delivered cost of         energy at the time and near where wind blows.)     -   For non-transactive neighbors, the cost of energy that applies         to any energy that is imported into the boundary of this         transactive node. (Note that if energy is exported rather than         imported during an IST interval n, it is not counted among         resources, so either or both the energy terms for this         sub-function should be set to zero.)     -   For transactive neighbors, the cost of delivered energy (e.g.,         the TIS) that applies to imported energy (e.g., the TFS). (This         is a special case where the input TIS and input TFS are read         from the Input Transactive Signal Buffer. A simple toolkit         function should be created to complete this task.)

The values C_(E,a,n) should be defensible representations of the delivered costs of energy {circumflex over (P)}_(G,a,n).

The sum of {circumflex over (P)}_(G,a,n) should represent the energy that is generated within or imported into this transactive node during IST interval n.

This sub-function may call upon various defined other local conditions that should be available as inputs from the Resource and incentive Input Buffer. The list of other local conditions that are expected by a give toolkit function should be known by the transactive node object and available from the Node State and Status Buffer.

Refer to sub-function 3.7 Calculate Output TIS to fully understand how the two outputs from the present sub-function will become incorporated into the formulation of TIS within the toolkit framework.

8.4 Assign Capacity Cost and Capacity—a sub-function of a toolkit resource and incentive function in which cost C_(C,b,n) (units: cost per power) is assigned to capacity limitations and costs that are triggered by capacities. The sub-function also captures the capacity {circumflex over (P)}_(C,b,n) (units: average power) to which the cost applies. In certain embodiments, one responsibility of a toolkit resource and incentive function is to calculate one of each of these two quantities for each current IST interval n. Either of the calculated quantities may be zero. The calculated values will differ depending on selected toolkit function and the resource or effect that is being modeled by the selected toolkit function.

Example capacity costs that should be included through this sub-function include

-   -   Costs that should be applied as equipment like power lines         become constrained     -   Imposed demand charges that become applied to the owners of this         transactive node.

Cost C_(C,b,n) should be defensible as cost that will be incurred upon a corresponding capacity {circumflex over (P)}_(C,b,n) that is predicted to occur during IST interval n.

This sub-function may call upon various defined other local conditions that should be available as inputs from the Resource and incentive Input Buffer. The list of other local conditions that are expected by a give toolkit function should be known by the transactive node object and available from the Node State and Status Buffer.

Refer to sub-function 3.7 Calculate Output TIS to fully understand how the two outputs from the present sub-function will become incorporated into the formulation of TIS within the toolkit framework.

8.5 Assign Infrastructure Cost—a sub-function of a toolkit resource and incentive function in which cost C_(I,c,n) (units: cost per time) is assigned to the provision of infrastructure at this transactive node, which costs are usually spread over quite long periods of time. In certain embodiments, one responsibility of toolkit resource and incentive function is to calculate and report one infrastructure cost output for each current IST interval n. Its value may be zero. The calculated value will differ depending on selected toolkit function and the resource or effect that is being modeled by the selected toolkit function.

Example infrastructure costs that may be used through this sub-function include

-   -   Initial purchase costs for equipment     -   Initial installation costs     -   Maintenance costs.

Refer to sub-function 3.7 Calculate Output TIS to fully understand how the output from the present sub-function will become incorporated into the formulation of TIS within the toolkit framework.

8.6 Assign Other Costs—a sub-function of a toolkit resource and incentive function in which other costs (units: cost) that cannot be represented by the other sub-functions are applied at this transactive node. In certain embodiments, one responsibility of a toolkit resource and incentive function is to calculate and report one such other cost output for each current IST interval n. Its value may be zero. The calculated value will differ depending on selected toolkit function and the resource or effect that is being modeled by the selected toolkit function.

This sub-function should not be used to bypass the other three sub-functions 8.3, 8.4, and 8.5. The other cost that is assigned by this sub-function should be a defensible component of the delivered cost of energy (e.g., the TIS) that will be formulated by process 3. Formulate TIS.

Refer to sub-function 3.7 Calculate Output TIS to fully understand how the output from the present sub-function will become incorporated into the formulation of TIS within the toolkit framework.

Inputs:

-   -   Current Input TIS and TFS should have been received in         process 1. Receive Transactive Signals and should be available         from the Input Transactive Signal Buffer. These inputs will be         treated the same as other energy terms.     -   Current other local conditions data that has been specified for         by the set of toolkit functions that are being applied at this         transactive node.     -   The list of toolkit functions that are to be applied in this         process, which list should be known to this transactive node         object and available from the Node State and Status Buffer.     -   The list of other local conditions data records that are         expected by the set of toolkit functions that are employed in         this process block, which list should be known by this         transactive node object and available from the Node State and         Status Buffer.

Outputs:

-   -   One paired energy cost and energy (C_(E,a)·{circumflex over         (P)}_(G,a)) series record placed into and available from the         Resource Schedules and Cost Buffer for each of the toolkit         functions that is applied within this process. (There are A         non-zero of these records used to represent imported and         generated energy.)     -   One paired capacity cost and capacity (C_(C,b)·{circumflex over         (P)}_(C,b)) series record placed into and available from the         Resource Schedules and Cost Buffer for each of the toolkit         functions that is applied within this process. (There are B         non-zero of these records where capacity costs are relevant.)     -   One infrastructure cost C_(I,c) series record placed into and         available from the Resource Schedules and Cost Buffer for each         of the toolkit functions that is applied within this process.         (There are C non-zero of these records where infrastructure         costs are relevant.)     -   One other cost C_(O,d) series record placed into and available         from the Resource Schedules and Cost Buffer for each of the         toolkit functions that is applied within this process. (There         are D non-zero of these records where other costs are relevant.)

Function/Process: The sub-functions were described above as they were being introduced. Sub-functions 8.3, 8.4, 8.5, and 8.6 are components of toolkit functions and may not be generically defined except through the characterization of their inputs and outputs.

Dependencies:

-   -   This process should find current current input transactive         signals from process 1. Receive Transactive Signals from within         the Input Transactive Signal Buffer.     -   This process expects current and relevant other local conditions         are available from the Resource and Incentive Input Buffer. The         list of example other local conditions records is known to this         transactive node object and available from the Node State and         Status Buffer.     -   This process expects that the relevant list of toolkit functions         will be known to the transactive node object and available from         the Node State and Status Buffer. The modular toolkit functions         themselves should be available at the transactive node.     -   This process expects that the current IST series will have been         calculated by process 2. Calculate New Transactive Signal         Intervals and will be available from the Current IST Series         Buffer.     -   This process outputs to the Resource Schedules and Cost Buffer         that are used for processes 10. Sum Total Predicted Resource and         by 3. Formulate TIS.

Notes:

-   -   A transactive node should instantiate at least one toolkit         function that redefines current transactive signals as energy         terms and places them into the Resource Schedules and Cost         Buffer.     -   General guidance should be that a transactive control and         coordination system can address economic decisions that interact         with the system somewhat slower than the update frequency. There         will occur an interim period where the Demonstration's system         will accept but not influence resource decisions that presently         involve markets and ancillary services that are not initially         tied into the transactive control and coordination system.         However, many such economic decisions may be addressed and         perhaps optimized by a transactive control and coordination         system as theories are developed to support doing so.     -   An alternative pathway has been provided for “Scheduled         Resources” to become entered into the Resource Schedules and         Cost Buffer. It is preferred, however, that even non-transactive         resources enter into the toolkit framework via a toolkit         function and this process 8. Calculate Applicable Toolkit         Resource and Incentive Functions. One of our most basis toolkit         functions should be one that represents traditional, bulk         generation.

FIG. 43 is a flowchart 5300 of an exemplary “calculate applicable toolkit resource and incentive functions” process.

9. Control Responsive Asset Systems

Purpose: Advise responsive asset systems of the actions that they should take during the present update interval in accordance with their planned responses for the current interval start time IST₀.

Applicability: This process should be completed at the update frequency by a transactive node that has at least one responsive asset system installed and responsive to the transactive control and coordination system.

Some transactive node owners will impose constraints on the dynamics with which their responsive asset systems may act, in which case this process may be completed less frequently than the update frequency. For example, certain responsive asset systems may be engaged only at the top of an hour and may remain engaged for minimum durations after that. Still others should be scheduled some time prior and are therefore not responsive to the update frequency. (The capabilities of various responsive asset systems are desirably addressed in the selected toolkit library functions 6.2m Toolkit Load Function.)

Sub-Functions and Sub-Processes: None. This process may be only described at a functional level due to the diversity of the responsive asset system that is to be controlled. Most of the actual control activities take place within the responsive asset systems themselves and according to the preferred practices of this transactive node's owner.

Inputs:

-   -   Advisory signal for current interval start time IST₀ that is         available from the Elastic Load Prediction Buffer. Each of these         inputs is expected to have one of three meanings depending upon         the capabilities of the targeted responsive asset         system—discrete binary, discrete multilevel, or continuous.

Outputs:

-   -   The principal output actually occurs outside the transactive         control and coordination system and outside this process but in         the final control of the assets within the target responsive         asset system.     -   The state or status of the responsive asset system may be         updated to the Node State and Status Buffer. For example, this         buffer may hold information about the availability of the system         or the amount of load that is presently available to be         controlled.

Function/Process: The process by which the advisory output found within the Elastic Load Prediction Buffer is to be converted into control actions for the present update interval will be quite unique to the responsive asset system and will take place within the system according to practices of this transactive node's owner.

Dependencies: If this transactive node possesses any responsive asset systems, then

-   -   This process expects to find a current advisory response for         each respective responsive asset system having been predicted         (planned) by its respective process 6. Calculate Applicable         Toolkit Load Functions and available in the Elastic Load         Prediction Buffer. Only the current interval start time IST₀ is         relevant to the actual, not the planned, control of a responsive         asset system.

Notes:

-   -   Note that the toolkit function that corresponds to a given         responsive asset system should state the information about the         system that should be maintained within the Node State and         Status Buffer.     -   Responsive asset systems may be either energy loads or         resources.     -   The transactive control and coordination system advises a         responsive asset system via this process, but it never directly         controls any responsive asset system. A responsive asset system         is not part of the transactive control and coordination system.     -   Responsive asset systems are very diverse. Even similar asset         systems use different approaches, practices, protocols, and         standards. One might realize an opportunity for standardization         in the three types of signals that will be used to advise         control actions for responsive asset systems—discrete binary,         discrete multilevel, and continuous.     -   For the Demonstration, responsive asset systems almost         exclusively refer to populations of individual assets. The         Demonstration's transactive control and coordination system         therefore will provide an advisory “control” signal to the         system, not to its individual assets. If use of transactive         control and coordination systems continues, it is feasible that         they will be extended down to individual assets. In principle, a         transactive control and coordination system is very scalable.

FIG. 44 is a flowchart 5400 of an exemplary “control responsive asset systems” process.

10. Sum Total Predicted Resources

Purpose: Sum the total energy resources entering the boundaries of this transactive node. The transactive node that has A resources

The sum produced by this process is used for two purposes in the toolkit framework: First, it is the divisor in process 3. Formulate TIS. Second, during process 4. Formulate TFS it is compared against the total load that is calculated by process 5. Sum Total Predicted Load, resulting in the net surplus or shortage of energy that should be allocated among the TFS of of transactive neighbors.

Applicability: This process should be completed at the update frequency by a transactive node.

Sub-Functions and Sub-Processes:

10.1 Interpolate Intervals Service Functions—a suite of service functions that may be called upon as they are desired to restate dated time series in terms of the current IST intervals. (These functions might be defined and used throughout the entire toolkit framework instead of uniquely defined for each process, as has been shown here.)

10.2 Sum Total Predicted Resource—sum of the A resources {circumflex over (P)}_(G,a,n) (units: average power) for each IST interval n. This sub-function should find a current representation of each summand from within the Resource Schedules and Cost Buffer. The expected set of summands should be known to this transactive node object and available from the Node State and Status Buffer. The sum should include electrical energy that is either generated within or imported into the boundaries of this transactive node during each IST interval n. Each of the summands should be found paired with an energy cost parameter C_(E) in the Resource Schedules and Cost Buffer.

Summands {circumflex over (P)}_(G,a,n) should include and represent

-   -   The TFS (units: average power) of each transactive neighbor from         which this transactive node will import energy during interval         n.     -   The average energy generated during IST intervals n from any         generator within the boundaries of this transactive node which         may be expected to influence the formulation of the TIS. That         is, its generated energy should be paid for and represented in         the transactive control and coordination system. (This will         include almost all generation resources. An exception will be         generation by end-use customers that displaces their load but         never should affect the cost energy in a way that would be         evident outside the customer premises.)     -   Energy imported during IST intervals n from electrically         connected neighbors who are not transactive neighbors.

$\begin{matrix} {{{Total}\mspace{14mu} {Predicted}\mspace{14mu} {Resource}} = {\sum\limits_{a = 1}^{A}\; {\hat{P}}_{G,a,n}}} & {{Process}\mspace{14mu} 10} \end{matrix}$

The output product from this sub-function is a single time series (units: average power) placed into the Total Predicted Resource Buffer each update interval.

10.3 Refresh Predicted Total Resource—early each update interval iteration, the most current contents of the Total Predicted Resource Buffer should be retrieved by this sub-function and restated using 10.1 Interpolate Intervals Service Functions in terms of the current IST interval set. These updated buffer contents are then available to be used by default should this transactive node fail for any reason to calculate total resource for the current iteration.

Inputs:

-   -   A multiplicity of resource components {circumflex over         (P)}_(G,a,n) (units: average energy) to be retrieved from the         Resource Schedules and Cost Buffer.     -   The identifiers of A resource components known by this         transactive node object and available from the Node State and         Status Buffer.     -   Current interval start time (IST) series available from the         Current IST Series Buffer.

Outputs:

-   -   Sum of resources

$\sum\limits_{a = 1}^{A}\; {\hat{P}}_{G,a,n}$

(units: average power) stored into the Total Predicted Resource Buffer. This output is a series of values, one for each IST interval.

Function/Process: The purpose of this process is to perform a mathematical sum, which has been described above as the sub-functions were being introduced.

Dependencies:

-   -   This process uses a current IST series to have been calculated         by process 2. Calculate New Transactive Signal Intervals and         available from the Current IST Series Buffer.     -   This process expects that current resource components         {circumflex over (P)}_(G,a,n) will have been placed into the         Resource Schedules and Cost Buffer by process 8. Calculate         Applicable Toolkit Resource and Incentive Functions. However,         the sub-function 8.3 Refresh Predicted Resources and Incentives         will have created a default set of inputs that may be used here         if current inputs cannot be calculated.     -   The current output of this process is used by process 3.         Formulate TIS and 4. Formulate TFS and is expected to be         available from the Total Predicted Resource Buffer. However,         some resiliency is provided by sub-function 10.3 Refresh         Predicted Total Resource, which calculates a default current         process output to be available from the Total Predicted Resource         Buffer should this process fail to create a current output by         the time it is used.

Notes:

-   -   Refer to processes 3. Formulate TIS and 4. Formulate TFS that         will give one a better sense of how the output of this process         is to be used.     -   The general term {circumflex over (P)}_(G,a,n) has been         introduced, in part, to deemphasize that there are multiple         types of such terms, including even the TFS at time it describes         imported energy. Altogether, these terms should include the         energy that is generated or imported within this transactive         node's boundary.

This process was originally considered as a sub-function within both processes 3 and 4. Because both processes performed the identical function, the function was elevated to a process at the toolkit-framework level so that the same sum may be used by both processes 3 and 4.

FIG. 45 is a flowchart 4500 of an exemplary “sum total predicted resources” process.

11. Control Responsive Resource

Purpose: Advise responsive resources of the actions that they should take during the present update interval in accordance with their planned responses for the current interval start time IST₀.

Applicability: This process should be completed at the update frequency by a transactive node that has at least one responsive resource. This process will be used infrequently until resources like bulk generators become responsive to a dynamic transactive control and coordination system.

Some resource owners will impose constraints on the dynamics with which their resources may act, in which case this process may be completed less frequently than the update frequency.

Sub-Functions and Sub-Processes: None. This process may be only described at a functional level due to the diversity of the resources that are to be controlled. Most of the responsibilities to engage resources lie with the resource systems themselves and not with processes of the toolkit framework.

Inputs:

-   -   Resource plans as formulated by certain toolkit functions within         the process 8. Calculate Applicable Toolkit Resource and         Incentive Functions.

Outputs:

-   -   The principal output actually occurs outside the resource system         and outside this process but in the final control of the         resource within the target resource system.     -   The state or status of the resource may be updated to the Node         State and Status Buffer. For example, this buffer may hold         information about the availability of the system or the amount         of resource that is presently available to be controlled.

Function/Process: The process by which the advisory output found within the Resource Schedules and Cost Buffer is to be converted into control actions for the present update interval will be quite unique to the responsive resource system and will take place within the system according to practices of the resource and transactive node owners.

Dependencies: If this transactive node possesses any responsive resource systems, then

-   -   This process expects to find a current advisory response for         each respective responsive resource system having been predicted         (planned) by its respective process 8. Calculate Applicable         Toolkit Resource and Incentive Functions and available in the         Resource Schedules and Cost Buffer. Only the current interval         start time IST₀ is relevant to the actual, not the planned,         control of a responsive resource system.

Notes:

-   -   Note that the toolkit function that corresponds to a given         responsive resource system should state the information about         the system that should be maintained within the Node State and         Status Buffer.     -   The transactive control and coordination system advises a         responsive resource system via this process, but it never         directly controls it. A responsive resource system is not part         of the transactive control and coordination system.     -   Responsive resource systems are very diverse. Even similar         systems use different approaches, practices, protocols, and         standards. One might realize an opportunity for standardization         in the three types of signals that will be used to advise         control actions for responsive resource systems—discrete binary,         discrete multilevel, and continuous.

FIG. 46 is a flowchart 4600 for an exemplary “control responsive resource” process.

6.2.4 SubAppendix A Interval Start Time Series Definition 6.2.4.1 Purpose

This section recommends a specific set of 57 Interval Start Times (IST) for use in example embodiments of the disclosed technology, including the Demonstration. The intervals range in duration from 5 minutes to 1 day. In this embodiment, the 57 ISTs define 56 intervals of varying duration, though other numbers of IST and different durations can be used.

6.2.4.2 Series of 57 Interval Start Times Defined

The first interval in a set of Interval Start Times is IST₀. While a transactive signal is being formulated, IST₀ is the next future time at which the minute hand of a clock will be at one of the 12 major divisions of an hour (e.g., on the hour, 5 minutes after the hour, 10 minutes after the hour, etc.).

The series of time intervals to be used by transactive signals during the Demonstration are as defined in Table 26. This set of 56 intervals is easily specified, creates the same numbers of intervals, exhibits increasing coarseness into the future, and will align well with dynamic market signals that are up to 1 hour in duration. Note that a 57^(th) IST (e.g., IST₅₆) has been added to unambiguously define the duration of the final, 56^(th) interval.

One variable-length interval resides at the boundary between sets of intervals having different durations. That is, there is a variable-length interval between 5- and 15-minute intervals, between 15-minute and 1-hour intervals, between 1- and 6-hour intervals, and between 6-hour and 1-day intervals. The duration of each variable-length interval varies between the durations of the two bounding intervals, inclusive. No intervals overlap in the resulting representation of the future.

Five-minute intervals are to be used 1 hour into the future; 15-minute intervals, 6 hours into the future; 1-hour intervals, 1 day into the future; 6-hour time intervals, 2 days into the future, and 1-day intervals, 3 to 4 days into the future.

TABLE 26 Example Interval Time Series for use with TIS and TFS Duration No. Intervals Interval Start Times 5 minutes 12 IST₀, IST₀ + 0:05, . . . , IST₁₀ + 0:05 15 minutes 20 Round(IST₁₁ + 0:15)*, IST₁₂ + 0:15, . . . , IST₃₀ + 0:15 1 hour 18 Round(IST₃₁ + 1:00)*, IST₃₂ + 1:00, . . . , IST₄₈ + 1:00 6 hours  4 Round(IST₄₉ + 6:00)*, IST₅₀ + 6:00, . . . , IST₅₂ + 6:00 1 day  2 Round(IST₅₃ + 1:00:00)*, IST₅₄ + 1:00:00, IST₅₅ + 1:00:00 >3 days 56 intervals 57 interval start times (IST) *This function “Round” indicates rounding down to the next 15-minute, 1-hour, 6-hour, or 1-day interval start time. Times are indicated as dd:hh:mm, e.g., days, hours, and minutes.

The intervals of several time series that adhere to this recommendation are shown in Table 27 for several example values of IST₀.

6.2.4.3 Pseudo Code for Example IST Series

The following formula guides the calculation of the IST series according to the specification in Table 26. The interval start times use the notation

IST_(n) [dd _(n) ,hh _(n) ,mm _(n)],  (A1)

where “dd” is days, “hh” is hours, and “mm” is minutes. The value n refers to the sequential, ordered number of the IST in its series. The total number of intervals in the series is N=56, where N is the last n.

IST≐{IST₀,IST₁,IST₂, . . . ,IST_(n), . . . ,IST_(N)}  (A2)

The following steps and pseudo code should help standardize calculation of the members of an IST time series. The function “truncate( )” indicates that the decimal parts of the result in the parentheses should be discarded.

(1) Calculate first element IST₀: Read present time t Set IST₀=t+0:05 Set mm_(o)=5*truncate (mm_(o)/5) (2) Calculate the IST series for remaining 5-minute intervals: For n=1 to 11

-   -   Set IST_(n)=IST_(n−1)+0:05     -   Next n         (3) Calculate the IST series for 15-minute intervals:

Set IST₁₂=IST₁₁+0:15

Set mm₁₂=15*truncate(mm₁₂/15) For n=13 to 31

-   -   Set IST_(n)=IST_(n−1)+0:15     -   Next n         (4) Calculate the IST series for 1-hour intervals:

Set IST₃₂=IST₃₁+1:00

Set mm₃₂=0 For n=33 to 49

-   -   IST_(n)=IST_(n−1)+1:00     -   Next n         (5) Calculate the IST series for 6-hour intervals:

Set IST₅₀=IST₄₉+6:00

Set hh₅₀=6*truncate(hh₅₀/6) For n=51 to 53

-   -   IST_(n)=IST_(n−1)+6:00     -   Next n         (6) Calculate the IST series for 1-day intervals:

Set IST₅₄=IST₅₃+1:00:00

Set hh₅₄=0

Set IST₅₅=IST₅₄+1:00:00

(7) Append the final IST that indicates the end of the last 1-day interval:

Set IST₅₆=IST₅₅+1:00:00 6.2.4.4 Example IST Series

Table 27 lists the 57 IST time series elements for 13 example values of IST₀. The number of intervals (56 for the Demonstration) and total described time duration, listed at the bottom of Table 27 for these examples, have been adopted as additional elements of the XML schema that has been designed for the Demonstration's transactive signals.

TABLE 27 Interval Start Times at Example Next Interval Start Times Interval # 0:00 0:05 0:10 0:15 0:30 0:45 1:00 3:00 5:00 6:00 12:00 18:00 1:00:00 5 min. 0 0:00 0:05 0:10 0:15 0:30 0:45 1:00 3:00 5:00 6:00 12:00 18:00 1:00:00 1 0:05 0:10 0:15 0:20 0:35 0:50 1:05 3:05 5:05 6:05 12:05 18:05 1:00:05 2 0:10 0:15 0:20 0:25 0:40 0:55 1:10 3:10 5:10 6:10 12:10 18:10 1:00:10 3 0:15 0:20 0:25 0:30 0:45 1:00 1:15 3:15 5:15 6:15 12:15 18:15 1:00:15 4 0:20 0:25 0:30 0:35 0:50 1:05 1:20 3:20 5:20 6:20 12:20 18:20 1:00:20 5 0:25 0:30 0:35 0:40 0:55 1:10 1:25 3:25 5:25 6:25 12:25 18:25 1:00:25 6 0:30 0:35 0:40 0:45 1:00 1:15 1:30 3:30 5:30 6:30 12:30 18:30 1:00:30 7 0:35 0:40 0:45 0:50 1:05 1:20 1:35 3:35 5:35 6:35 12:35 18:35 1:00:35 8 0:40 0:45 0:50 0:55 1:10 1:25 1:40 3:40 5:40 6:40 12:40 18:40 1:00:40 9 0:45 0:50 0:55 1:00 1:15 1:30 1:45 3:45 5:45 6:45 12:45 18:45 1:00:45 10 0:50 0:55 1:00 1:05 1:20 1:35 1:50 3:50 5:50 6:50 12:50 18:50 1:00:50 11 0:55 1:00 1:05 1:10 1:25 1:40 1:55 3:55 5:55 6:55 12:55 18:55 1:00:55 15-min. 12 1:00 1:15 1:15 1:15 1:30 1:45 2:00 4:00 6:00 7:00 13:00 19:00 1:01:00 13 1:15 1:30 1:30 1:30 1:45 2:00 2:15 4:15 6:15 7:15 13:15 19:15 1:01:15 14 1:30 1:45 1:45 1:45 2:00 2:15 2:30 4:30 6:30 7:30 13:30 19:30 1:01:30 15 1:45 2:00 2:00 2:00 2:15 2:30 2:45 4:45 6:45 7:45 13:45 19:45 1:01:45 16 2:00 2:15 2:15 2:15 2:30 2:45 3:00 5:00 7:00 8:00 14:00 20:00 1:02:00 17 2:15 2:30 2:30 2:30 2:45 3:00 3:15 5:15 7:15 8:15 14:15 20:15 1:02:15 18 2:30 2:45 2:45 2:45 3:00 3:15 3:30 5:30 7:30 8:30 14:30 20:30 1:02:30 19 2:45 3:00 3:00 3:00 3:15 3:30 3:45 5:45 7:45 8:45 14:45 20:45 1:02:45 20 3:00 3:15 3:15 3:15 3:30 3:45 4:00 6:00 8:00 9:00 15:00 21:00 1:03:00 21 3:15 3:30 3:30 3:30 3:45 4:00 4:15 6:15 8:15 9:15 15:15 21:15 1:03:15 22 3:30 3:45 3:45 3:45 4:00 4:15 4:30 6:30 8:30 9:30 15:30 21:30 1:03:30 23 3:45 4:00 4:00 4:00 4:15 4:30 4:45 6:45 8:45 9:45 15:45 21:45 1:03:45 24 4:00 4:15 4:15 4:15 4:30 4:45 5:00 7:00 9:00 10:00 16:00 22:00 1:04:00 25 4:15 4:30 4:30 4:30 4:45 5:00 5:15 7:15 9:15 10:15 16:15 22:15 1:04:15 26 4:30 4:45 4:45 4:45 5:00 5:15 5:30 7:30 9:30 10:30 16:30 22:30 1:04:30 27 4:45 5:00 5:00 5:00 5:15 5:30 5:45 7:45 9:45 10:45 16:45 22:45 1:04:45 28 5:00 5:15 5:15 5:15 5:30 5:45 6:00 8:00 10:00 11:00 17:00 23:00 1:05:00 29 5:15 5:30 5:30 5:30 5:45 6:00 6:15 8:15 10:15 11:15 17:15 23:15 1:05:15 30 5:30 5:45 5:45 5:45 6:00 6:15 6:30 8:30 10:30 11:30 17:30 23:30 1:05:30 31 5:45 6:00 6:00 6:00 6:15 6:30 6:45 8:45 10:45 11:45 17:45 23:45 1:05:45 1-hr. 32 6:00 7:00 7:00 7:00 7:00 7:00 7:00 9:00 11:00 12:00 18:00 1:00:00 1:06:00 33 7:00 8:00 8:00 8:00 8:00 8:00 8:00 10:00 12:00 13:00 19:00 1:01:00 1:07:00 34 8:00 9:00 9:00 9:00 9:00 9:00 9:00 11:00 13:00 14:00 20:00 1:02:00 1:08:00 35 9:00 10:00 10:00 10:00 10:00 10:00 10:00 12:00 14:00 15:00 21:00 1:03:00 1:09:00 36 10:00 11:00 11:00 11:00 11:00 11:00 11:00 13:00 15:00 16:00 22:00 1:04:00 1:10:00 37 11:00 12:00 12:00 12:00 12:00 12:00 12:00 14:00 16:00 17:00 23:00 1:05:00 1:11:00 38 12:00 13:00 13:00 13:00 13:00 13:00 13:00 15:00 17:00 18:00 1:00:00 1:06:00 1:12:00 39 13:00 14:00 14:00 14:00 14:00 14:00 14:00 16:00 18:00 19:00 1:01:00 1:07:00 1:13:00 40 14:00 15:00 15:00 15:00 15:00 15:00 15:00 17:00 19:00 20:00 1:02:00 1:08:00 1:14:00 41 15:00 16:00 16:00 16:00 16:00 16:00 16:00 18:00 20:00 21:00 1:03:00 1:09:00 1:15:00 42 16:00 17:00 17:00 17:00 17:00 17:00 17:00 19:00 21:00 22:00 1:04:00 1:10:00 1:16:00 43 17:00 18:00 18:00 18:00 18:00 18:00 18:00 20:00 22:00 23:00 1:05:00 1:11:00 1:17:00 44 18:00 19:00 19:00 19:00 19:00 19:00 19:00 21:00 23:00 1:00:00 1:06:00 1:12:00 1:18:00 45 19:00 20:00 20:00 20:00 20:00 20:00 20:00 22:00 1:00:00 1:01:00 1:07:00 1:13:00 1:19:00 46 20:00 21:00 21:00 21:00 21:00 21:00 21:00 23:00 1:01:00 1:02:00 1:08:00 1:14:00 1:20:00 47 21:00 22:00 22:00 22:00 22:00 22:00 22:00 1:00:00 1:02:00 1:03:00 1:09:00 1:15:00 1:21:00 48 22:00 23:00 23:00 23:00 23:00 23:00 23:00 1:01:00 1:03:00 1:04:00 1:10:00 1:16:00 1:22:00 49 23:00 1:00:00 1:00:00 1:00:00 1:00:00 1:00:00 1:00:00 1:02:00 1:04:00 1:05:00 1:11:00 1:17:00 1:23:00 6-hrs. 50 1:00:00 1:06:00 1:06:00 1:06:00 1:06:00 1:06:00 1:06:00 1:06:00 1:06:00 1:06:00 1:12:00 1:18:00 2:00:00 51 1:06:00 1:12:00 1:12:00 1:12:00 1:12:00 1:12:00 1:12:00 1:12:00 1:12:00 1:12:00 1:18:00 2:00:00 2:06:00 52 1:12:00 1:18:00 1:18:00 1:18:00 1:18:00 1:18:00 1:18:00 1:18:00 1:18:00 1:18:00 2:00:00 2:06:00 2:12:00 53 1:18:00 2:00:00 2:00:00 2:00:00 2:00:00 2:00:00 2:00:00 2:00:00 2:00:00 2:00:00 2:06:00 2:12:00 2:18:00 1-day 54 2:00:00 3:00:00 3:00:00 3:00:00 3:00:00 3:00:00 3:00:00 3:00:00 3:00:00 3:00:00 3:00:00 3:00:00 3:00:00 55 3:00:00 4:00:00 4:00:00 4:00:00 4:00:00 4:00:00 4:00:00 4:00:00 4:00:00 4:00:00 4:00:00 4:00:00 4:00:00 56 4:00:00 5:00:00 5:00:00 5:00:00 5:00:00 5:00:00 5:00:00 5:00:00 5:00:00 5:00:00 5:00:00 5:00:00 5:00:00 Totals 56 4:00:00 4:23:55 4:23:50 4:23:45 4:23:30 4:2315 4:23:00 4:21:00 4:19:00 4:18:00 4:12:00 4:06:00 4:00:00 Note 1: All times in this table are presented in the format dd:hh:mm, where “dd”, “hh,” and “mm” are days, hours, and minutes after time 00:00:00. Note 2: The row “Totals” is (1) the total number of intervals (not IST) being represented and (2) the total amount of time represented within the given time series.

6.2.5 SubAppendix C Toolkit Function Specification Template

This example template can be completed for each toolkit function and can be posted to a common library. The following template items are used in this template:

-   -   Function Name     -   Function Version and Date     -   Description—narrative description of what is to be performed or         accomplished by the function     -   Block Function Model—input parameters, output parameters, and         actors     -   Pseudo Code Implementation—parametric mathematical model or         function that explains how function is implemented within the         toolkit framework. Reference implementations that instantiate         this named toolkit function should accomplish the algorithm that         is laid out by this pseudo code. If that is for any reason         impossible, another toolkit function should be named and         described.     -   Reference Implementation(s) Available—example implementation         code that instantiates this function. The implementations should         be referenced here in proper, complete citations.     -   Future Improvements—recommend any future improvements that have         been identified for this function.

6.2.6 SubAppendix C Standard Advisory Output Control Signal

Each toolkit function that models a system of responsive assets is responsible to advise the system of assets when and to what degree it should respond. Each such toolkit function should therefore calculate a time series that states a degree of response for each current interval start time (IST). The recommendation has been summarized in Table 28.

The following advisory signal format can be used as a standard for toolkit functions. This method accommodates advisory responses from binary (curtailed vs. normal) to several discrete levels (e.g., response level #1, response level #2, . . . ) to a continuum of possible responses (e.g., generate at 56% of nameplate capacity for the specified interval).

The advisory signal has been defined as a signed value to allow its application to responsive loads, responsive generation, and energy storage resources. Positive values are used when the recommended control action should increase the availability of energy by either increasing generation or by reducing load; a negative number is used when the recommended control actions should reduce generation or increase load.

The signal is quite intentionally defined in respect to a byte representation. The three most significant bits have been highlighted in Table 28 to emphasize that these bits fully represent the eight states of any asset system that has four levels of response available to it (the additional bit represents charge/discharge direction). These bits may therefore be used quite directly by simple assets or asset systems that possess limited computational capability.

1. A signed byte value is assumed (e.g., a signed 8-bit representation [−127, 127]). (For symmetry, the value −128 has not assigned. In gate logic, the use of one's complement interpretation of negative numbers accomplishes this symmetry and may be advantageous especially for controlling very simple, small assets.) 2. Positive values refer to generation [0, 127]; negative values refer to load [−0, −127]. 3. The toolkit function is responsible to state a response level for each future interval, consistent with its modeled influences on transactive signals. If the asset system's number of available response levels is known with certainty at the time the toolkit function is selected, the toolkit function may prescribe a representation for each response level. 4. The asset system, or alternatively “glue” code between the toolkit function and the asset system, is responsible to interpret the advisory signal. Interpretation of the advisory signal should be made by first dividing the respective generation or load range by the number of response levels that are available from the responsive asset system. Then the asset system may determine into which of its available levels the advisory signal belongs. If a continuum of available responses exists for this asset system, the full range of the continuum should be meaningfully applied to the full nameplate rating or total population, such that the signal range is applied to the entire available resource or load range.

Example #1: Suppose toolkit load function TKLF_(—)1.4 has been selected to model the behavior of a set of wind turbines. The behaviors of these wind turbines are not elastic and would therefore not be expected to change their operations in respect to transactive control. This toolkit function should not calculate and send any advisory control signal to the set of wind turbines. The set of wind turbines should not expect to receive any advisory control signals.

Example #2: A toolkit load function is being designed to model a system of demand responsive water heaters. The system of water heaters should be curtailed as a group. One of the outputs from the toolkit load function is designed to be a time series of advisory signals selected from the domain {0, 127}, which members represent normal and curtailed operation, respectively, for this load. (In certain implementations, and as discussed herein, a series of 56 intervals can be used, where each interval is defined by its interval start time (IST). See, e.g., Subappendix A.) The selection of the extreme advisory signals for a load having only two levels is wise because the signals will prescribe a reasonable binary response regardless of the capabilities of the asset system to which the signal is sent. The curtailable water heater system looks for signals in the ranges [0, 63] (normal operation) or [64, 127] (curtailed operation). The range [−0, −127] should be ignored (e.g., normal operation) by this responsive asset system because it can only curtail its load; it cannot increase its load in response to transactive control signals.

Example #3: A toolkit load function is created for a small residential battery storage system that has only three available response levels—fully charging, resting, and fully discharging. The function should state a time series of advisory signals to the battery system, perhaps specifying from among a set of three outputs in the set {−127, 0, 127}, which represent the three states fully charging, resting, and fully discharging, respectively. The battery system should be configured to expect one of three ranges of advisory signals [−127, −64] (charging), [−63, 63] (resting), or [64, 127] discharging.

Example #4: Another toolkit load function is created to model a battery storage storage system, but this function expects to be paired with a battery system that can operate through a continuum of responses from fully charging to fully discharging. The function creates advisory signals accordingly at any integer value in the range [−127, 127]. The battery system converts these numbers into percentages of its range of charge and discharge rates, which is done easily by dividing through by the integer 127. For example, the advisory signal value 26 is converted to 26/127, or 20.5% of its full available discharge rate.

Example #5: The small battery system of Example #3 is paired with the toolkit load function of Example #4. Even though the toolkit function calculates a continuum of responses, the battery system that has only three available response levels may nonetheless respond sensibly to the advisory signal that it receives. However, because the asset's responses do not match the responses that will have been modeled by the toolkit function, the toolkit function will not correctly predict the load (and generation) that will be supplied by this battery system.

TABLE 28 Interpretation of Recommended Advisory Signal Binary Three Four Signal Byte Levels Levels Levels . . . Continuum → Increase 127 0111 | 1111 Level #2 of 2 Level #3 of 3 Level #4/4 . . .  100.0% Generation/ 126 0111 | 1110 (Generate/  99.2% Decrease . . . . . . Curtail Load) . . . Consumption → 96 0110 | 0000  75.6% 95 0101 | 1111 Level #3 of 4  74.8% . . . . . . . . . 85 0101 | 0101  66.9% 84 0101 | 0100 Level #2 of 3  66.1% . . . . . . . . . 64 0100 | 0000  50.4% 63 0011 | 1111 Level #1 of 2 Level #2 of 4  49.6% . . . . . . (Normal) . . . 43 0010 | 1011  33.9% 42 0010 | 1010 Level #1 of 3  33.1% . . . . . . . . . 32 0010 | 0000  25.2% 31 0001 | 1111 Level #1/4  24.4% . . . . . . . . . 1 0000 0001   0.8% 0 0000 | 0000   0.0% ← Decrease −0 1111 | 1111 Level #1 of 2 Level #1 of 3 Level #1/4 . . .  −0.0% Generation/ −1 1111 | 1110 (Normal)  −0.8% Increase . . . . . . . . . Consumption ← −31 1110 | 0000  −24.4% −32 1101 | 1111 Level #2 of 4  −25.2% . . . . . . . . . −42 1101 | 0101  −33.1% −43 1101 | 0100 Level #2 of 3  −33.9% . . . . . . . . . −63 1100 | 0000  −49.6% −64 1011 | 1111 Level #2 of 2 Level #3 of 4  −50.4% . . . . . . (Curtail . . . −84 1010 | 1011 Generatation/  −66.1% −85 1010 | 1010 Add Load) Level #3 of 3  −66.9% . . . . . . . . . −95 1010 | 0000  −74.8% −96 1001 | 1111 Level #4/4  −75.6% . . . . . . . . . −126 1000 | 0001  −99.2% −127 1000 | 0000 −100.0%

6.2.7 SubAppendix D Toolkit Functions

This subappendix lists and describes example toolkit functions that can be implemented in embodiments of the disclosed technology. Two types of toolkit functions have been defined:

(1) Resource and incentive toolkit functions—used to capture the influences of energy resources and other influences upon the transactive control and coordination system's incentive signal (e.g., the TIS) (2) Load functions—used to capture the influence of both elastic (e.g., “responsive”) and inelastic loads on the transactive control and coordination system's feedback signal (e.g., the TFS).

SubAppendix B provides a template by which the toolkit functions themselves and specific reference implementations of the toolkit functions should be documented. Thereafter, these toolkit functions may be selected from a “library” of such available toolkit functions and applied at any applicable transactive nodes.

The outputs of toolkit functions constitute an interoperability boundary as the project strives to standardize the information that flows from the toolkit functions into the toolkit framework at many levels of an interoperability information stack.

6.2.8 Resource and Incentive Toolkit Functions

The example resource and incentive toolkit functions listed in Table 29 are defined and represent as instantiations of 8. Calculate Applicable Toolkit Resource and Incentive Functions within the toolkit framework. Toolkit functions having the same name and number should share a common purpose and same general approach and should promise the same set of outputs into the toolkit framework. Versioning may be used for variants of these functions that differ slightly in approach, in complexity, or by the nature of expected inputs.

In Table 29, an attempt was made to organize the functions by type and level. Following this enumeration, Function 1.1.1 would be a special implementation of Function 1.1, which is a special implementation of Function 1.0.

Each toolkit function should be defined by appropriate documentation following the template in SubAppendix B.

TABLE 29 List of Resource and Incentive Toolkit Functions Name, No. & Where Version Purpose Applied Inputs Outputs 1.0 Imported Electrical Energy 1.1 Non- Accommodate Peripheral Current IST Time series of Transactive importation of transactive time series. energy Imported electrical nodes that are Historical index exchange P_(G) Energy energy from scheduled to at price or cost through this outside this times receive information corridor using transactive bulk electrical about this the current set node from energy from exchange, of IST entities that are outside the which can intervals. not themselves boundaries of inform Time series of transactive this transactive simulation of predicted cost nodes-are not control and current energy of energy participants in coordination costs for this through this this transactive system. exchange of corridor C_(E). control and energy. coordination Historical system. energy exchanges for this corridor. Alternatively, seasonally- adjusted daily and weekly exchange schedules from which simulations may be informed and improved. Intertie exchange schedules (may be estimated from an informed simulation). Price index that represents the current delivered cost of electrical energy through this exchange corridor if such current information can be obtained. Day of week and holiday schedules. 1.2 Transactive Converts A transactive Current IST TIS restated Imported transactive node should time series. as energy Energy signals from restate the Transactive terms C_(E .) transactive transactive incentive TFS restated neighbors into signals that it signals (TIS) as energy framework receives in from each terms P_(G) for parameter terms of toolkit transactive the intervals outputs that are framework neighbor. during which expected by the parameters. Transactive the TFS toolkit This toolkit feedback represents framework. function is so signals (TFS) imported basic that it from each energy. may be treated transactive as part of the neighbor. toolkit framework. 2.0 Renewable Energy Resource 2.1 Wind Encourage use Applicable to Current IST Predicted Energy of wind-farm- energy time series. average wind scale energy produced by a Historical wind power P_(G) when and near wind farm. May farm power using intervals where it is be applied to output time of the current generated. aggregated series, which IST time The cost of output from may be used to series. supplying multiple wind tune and refine Infrastructure renewable farms. predictions. cost time energy is Use this Actual current series C_(I) using applied as an function at wind farm intervals of the infrastructure transactive power output, current IST cost, not as an nodes where which may be time series. energy cost, in owners own or used to tune (Infrastructure order to represent one and refine costs are not encourage the or more wind predictions. expected to be consumption of farms. Predicted wind especially wind energy. Transactive speed and dynamic, but it nodes that direction time is specified as have and series. a time series represent wind Predicted for farm energy relative humidity consistency.) that is time series. produced Predicted air within their density time electrical series. boundaries. Predicted resource availability (accounts for effects of maintenance and curtailment shedding). Function that predicts wind farm power output from these conditions. Estimated amortized wind farm infrastructure expense, including operational and maintenance expenses, which estimates will be used to state the infrastructure parameter. If the costs of these specific wind farms are unavailable, secondary sources of such estimates may be used. (Infrastructure costs are probably the only costs that will be used by this function, so in some emobdiments, the infrastructure cost can be estimated from the total, long- term expense of supplying wind energy from the resource. By doing so, the effective cost of the wind energy will be incorporated over time using a meaningful cost.) 2.2 Solar Encourage use Applicable to Current IST Predicted Energy of solar energy medium- or time series. average solar when and near large-scale Historical solar power P_(G) where it is solar site power using intervals generated. generation. output time of the current The cost of (Small solar series, which IST time supplying sites may be may be used to series. renewable better tune and refine Infrastructure energy is addressed as predictions. cost time applied as an negative load Actual current series C_(I) using infrastructure toolkit solar site power intervals of the cost, not as an functions, output, which current IST energy cost, in especially if may be used to time series. order to such energy tune and refine (Infrastructure encourage the offsets and predictions. costs are not consumption of reduces load Predicted solar expected to be solar energy. at this insolation time especially location.) series. dynamic, but it Transactive Predicted wind is specified as nodes where speed and a time series owners own direction time for medium- or series. consistency.) large-scale Predicted air solar density time generation. series (may or Transactive may not be nodes that used). have and Predicted represent the resource energy from availability, solar sites which accounts within their for maintenance electrical outages. boundaries. Function that predicts solar power from these inputs. Estimated amortized solar site infrastructure expense, including operational and maintenance expenses, which estimates will be used to state the infrastructure parameter. If the costs of these specific solar sites are unavailable, secondary sources of such estimates may be used. Infrastructure costs are probably the only costs that will be used by this function, so in some embodiments, the infrastructure cost should be estimated from the total, long- term expense of supplying solar energy from the resource. By doing so, the effective cost of the solar energy will be incorporated over time using a meaningful cost. 2.3 TBD, based on Transactive Current IST Predicted Hydropower input expected nodes that own time series. average from a or represent Scheduled hydropower P_(G) hydropower hydropower hydropower time series working group generation. generation using the that has been Transactive production intervals of the asked to nodes that targets current IST formulate this have or Actual time series function. represent hydropower Predicted Perhaps, hydropower generation, if infrastructure encourage use generation available. cost time of hydroelectric within their Day of week series C_(I) using energy when electrical and holidays. intervals of the and near where boundaries. current interval it is generated. start time (IST) This function series. should at least (Infrastructure represent costs are not federal expected to be hydropower of especially the region but dynamic, but it should strive to is specified as represent all a time series regional for hydropower. consistency.) 3.0 Fossil Represent Transactive Current IST Predicted Generation effect of fossil- nodes that own time series. average fuel generation or represent Predicted cost generated on electrical fossil of fuel, which power P_(G) time energy cost. generation. may be either series using May be used constant or a the intervals of for aggregated dynamic time the current IST sets of fossil series, time series. generation depending on Corresponding resources. the fuel. predicted Should apply Generator energy costs to fossil dispatch of generated generation schedule(s). power C_(E) within the Fuel heating using the electrical value (probably intervals of the boundary of a a constant). current IST transactive Plant efficiency time series. node. (probably a Predicted constant, but infrastructure may be a cost C_(I) time function of series using generated the intervals of power and other the current IST inputs). time series. Outdoor (Infrastructure temperature cost is not time series. expected to be Input feed especially temperature dynamic, but it time series. is specified as Representative a time series amortized for infrastructure consistency.) cost. (In some cases, the infrastructure costs will be stated as functions of many variables, including local costs of money, taxes, regulations, etc.) Function by which inputs are used to predict power output. Day of week and holidays. 4.0 General Represent bulk Almost every TBD. Estimate Infrastructure Infrastructure influence of transactive of present cost time Cost infrastructure node could use infrastructure series C_(I). investments on this function. value amortized delivered cost over an of electrical applicable energy where it horizon. might be Calculation impracticable to should include track individual effects of local infrastructure influences like a components. utility's normal estimate of useful equipment lifetime. Estimates should be calibrated against known ways in which long-term infrastructure costs are addressed. 5.0 System Constraints 5.1 Discourage Transmission Predicted Capacity cost Transmission consumption zone flowgate power. C_(C) and Flowgate downstream transactive Formula by corresponding from, and nodes on which flowgate flowgate encourage either side of a power will affect capacity P_(C). consumption flowgate. TIS each upstream from, transactive a flowgate node. transmission Additional constraint. inputs may be Costs should be considered for grounded future versions, somehow in but the initial actual costs version should that would be be kept very incurred if simple. flowgate constraints were to be violated. 5.2 Equipment Discourage Transactive Predicted Predicted and Line consumption of nodes that are capacity to capacity cost Constraints energy in a position to which this time series C_(C) downstream mitigate their function applies. and from constraints by Function which corresponding constrained increasing the estimates the capacity time distribution delivered cost cost impacts of series P_(C). equipment, of energy to exceeding the including downstream capacity distribution transactive constraint. lines. nodes. Intended to be used where constraints may be correlated to specific equipment. Does not apply to transmission flowgates. 6.0 System Energy Losses 6.1 Incorporate the Presently a low Function by Lost energy Transmission effect of line priority. which TFS and term of type Losses losses on cost Intended for non-transactive P_(G). of delivered application in imported and energy in transmission exported power transmission zones. indicate long- zones May be distance defined and transmission applied for losses across a major transmission transmission zone. across Representative transmission fraction of zone transmitted transactive power to be nodes. lost, which may be applied as a representative resistance at a stated transmission voltage. 6.2 Distribution Incorporate the Presently a low Function by Lost energy Losses effect of line priority. which TFS and term of type losses on cost Intended for non-transactive P_(G). of delivered application in imported and energy in the topology at exported power distribution and locations other can be used to other locations than define energy where specific transmission losses in lossy zones. specific equipment can Applied where equipment or be identified. losses may be systems. Reflects that attributed to the value of specific dissipated equipment or energy is lost. systems. 7.0 Demand Charges 7.1 BPA Utility Subproject Predicted Capacity time Demand transactive transactive capacity to series P_(C) that Charges node takes nodes where which demand causes the steps to owners are charges may demand manage peak utilities that are apply. charges. loads that may subject to Historical utility Capacity cost incur demand demand load during the time series C_(C) charges. charges from current month, that Help a utility BPA. including prior corresponds to reduce its peak hour. the capacities. monthly peak. Function by (The which cost capacities impact of may, or may capacity may be not, also be predicted. TFS values, Day of week depending on and holidays. the boundaries of a given transactive node.) 7.2 Seattle City This function UW's SCL peak Average power Light Demand predicts the transactive demand rate capacity P_C Charges impact of node. [$/kW] as defined by demand SCL off-peak the charges that the demand rate Transactive Seattle City [$/kW] Node Light (SCL) will Transactive Framework apply to the Feedback [kW]. University of Signal (TFS) Capacity cost Washington [kW] C_C as (UW) Interval Start defined by the Times (ISTs) Transactive A scaling factor Node K by which the Framework effect of the [$/kW]. demand charges may be scaled. 8.0 Market Impacts 8.1 Spot Utility Subproject TBD. TBD. Market Impacts transactive transactive Perhaps, Perhaps, node takes nodes where predicted capacity time steps to owners are capacity to series P_(C) that mitigate utilities that are which spot causes the (optimize) the subject to the market impacts spot market predicted impacts of spot may apply. impacts and impacts that it market trading. capacity cost will likely incur time series C_(C) on spot that markets. corresponds to the capacities. This function might use other cost time series C_(O) if it cannot be stated in terms of energy, capacity, or infrastructure.

6.2.9 Load Toolkit Functions

Load toolkit functions are instantiated as 6. Calculate Applicable Toolkit Load Functions within the toolkit framework. The load being described by these functions may be either elastic (responsive to the TIS) or inelastic (not responsive to the TIS). These functions should not have direct influence and effect on the calculation of TIS as this transactive node; functions that will affect the formulation of TIS should be stated as resource or incentive toolkit functions.

The Demonstration attempts to define and use a minimum adequate set of load toolkit functions. Therefore, implementers should select and apply the most general function that can describe the expected behaviors. In Table 30, an attempt was made to organize the functions by type and level. Following this enumeration, Function 1.1.1 would be a special implementation of Function 1.1, which is a special implementation of Function 1.0. Function 1.0 is more general that is the Function 1.1 under it.

The most general functions have been stated as

1. Bulk inelastic load—large sets of load that is not affected by the TIS 2. General event-driven demand response (DR)—sets of asset systems that are infrequently affected by the TIS. These asset systems are affected in a binary, on/off way or occasionally provide a limited number of discrete response levels. Specific examples may include distribution voltage control, water heater programs, smart appliance programs, and distributed generation. 3. General time-of-use (TOU) DR—sets of asset system that are affected by the TIS according to a daily cycle. These asset systems are affected in a binary, on/off way or occasionally provide a limited number of discrete response levels. Examples may include distribution voltage control, water heater programs, smart appliance programs, and battery storage.

General real-time (RT) DR—sets of asset systems that are affected by the TIS and employ a continuum of possible responses. Examples may include energy portals and battery storage.

TABLE 30 List of Load Toolkit Functions Name, No. & Where Version Purpose Applied Inputs Outputs 1.0 Bulk Predict bulk, Transactive Current IST time Predicted Inelastic undifferentiated nodes where series. inelastic load Load inelastic it is preferred (LI_01) Historical for each load in the to predict load for this current IST most general undifferentiated modeled interval. sense. bulk load. population Places where (LI_02) Present specific load (average models to power) for this predict the population of behaviors of inelastic load differentiated (LI_03) load Predicted components outdoor are not temperature time possessed. series Nearly every (LI_04) subproject Predicted could use this insolation time function. series (LI_05) Predicted wind speed and direction time series (LI_06) Weekday, weekend day, and holiday indicator (LI_08) Typical seasonally- adjusted daily load profile (LI_07) Average daily load (a constant for the prediction horizon) 1.1 Bulk Predict the Transactive Current IST time Predicted Commercial load of bulk nodes that series. inelastic load Load inelastic represent (LI_01) Historical for each commercial inelastic load current IST load. May be electrical load (LI_02) Actual interval. used to from measured load represent aggregated (LI_03) sets of commercial Predicted aggregated loads. outdoor commercial Most temperature time loads, even subproject series ones with transactive (LI_04) diverse nodes will use Predicted membership. this function. insolation time Does not series model (LI_05) underlying Predicted wind commercial speed and buildings and direction time processes. series This model (LI_06) Day of does not week and include holidays elastic (LI_07) Average behaviors daily load that would (constant during be expected the prediction to respond to horizon) a TIS. (LI_08) Typical daily load profile 1.2 Bulk Predict the Transactive Current IST time Predicted Industrial load of bulk nodes that series. inelastic load Load industrial represent (LI_01) Historical for each load types. electrical load load current IST Does not from (LI_02) Actual interval. model aggregated measured load underlying industrial (LI_03) industrial loads. This Predicted processes. function does outdoor not require temperature time underlying series industrial (LI_04) processes to Predicted be understood insolation time and modeled. series May be (LI_05) applied to Predicted wind multiple speed and aggregated direction time industrial series loads. (LI_06) Day of Many week and subproject holidays transactive (LI_07) Average nodes that daily load (a include constant during industrial the prediction loads may horizon) choose to use (LI_08) Typical this function. daily load profile (LI_09) Fractional representation of common commercial building types 1.3 Bulk Predict the Transactive Current IST time Predicted Residential load of bulk nodes that series. inelastic load Load residential wish to (LI_01) Historical for each load type. represent load current IST Predict load electrical load (LI_02) Actual interval. of residential for groups of measured load feeders or residences (LI_03) groups of like those on Predicted residential residential outdoor feeders. feeders. temperature time Does not Applied to series necessarily residential (LI_04) model loads that are Predicted individual not insolation time residences responsive to series or the the TIS (e.g., (LI_05) underlying inelastic Predicted wind behaviors of residential speed and homes and populations). direction time their Individual series occupants. residences (LI_06) Day of Models and week and inelastic underlying holidays residential resident (LI_10) Number load only. behaviors are of single- and not modeled. multiple-family Almost every units subproject transactive node is expected to use this function for its residential customers who do not respond elastically. 1.4 Small Predict the Locations that Current IST time Time series Wind “negawatts” host relatively series. output power Generator to be small wind (LI_11) Historical for each IST Negative produced by generators or power interval. This is Load small wind wind sites that production time an inelastic energy primarily series load resources. offset a larger (LI_12) component This function electrical load. Predicted wind because it is is preferred speed and not a function where a direction time of the TIS. relatively series for a No control small representative output is sent amount of tower height to renewable wind (LI_13) Historical generators. renewable wind speed and Renewable generation direction at a generators are offsets load representative not responsive at a location. tower height to the If the energy near the wind transactive from a wind generation control and energy (LI_14) coordination resource Measured wind system. should affect speed and TIS at this direction at a and representative electrically tower height downstream near the locations, the generation site energy from (LI_15) Historical this resource relative humidity should be time series incorporated (LI_16) with a Predicted resource and relative humidity incentive time series toolkit (LI_17) Historical function air density time instead (See series Table 29:). (LI_18) Predicted air density time series (LI_X) Effective total cross- sectional area (LI_X) Wind conversion efficiency curve (LI_19) Season, or day of year (LI_20) Total nameplate or “typical” power capacity (LI_X) Predicted resource availability 1.5 Small- Predict and Locations that Current IST time Time series Scale represent host relatively series. output power Distributed “negawatts” small fossil (LI_01) Historical for each IST Generator load from fuel power interval. Negative one or more generators production Distributed Load relatively that are not (IL_X) Resource generators of small influenced in schedule this toolkit distributed their operation (LI_20) function are generators by the TIS. Nameplate or not responsive that target power to the consume production transactive hydrocarbon magnitude. control and fuels at this (LI_6) Day of coordination location. week and system, but These holidays they may generators (LI_IX) Predicted respond to are not resource other purposes influenced by availability and objectives the TIS. of their owners If the (e, g., periodic influence of maintenance a distributed schedules, generator feedstock should availability). No directly affect control output the TIS at a is sent to these transactive distributed node, select generators. an appropriate source and incentive toolkit function from Table 29:. 1.6 Small- Predict the Locations that Current IST time Time series Scale Solar “negawatts” host relatively series. average output Generator to be small solar (LI_01) Historical power for each Negative produced by generators power IST interval. Load small solar that primarily production No control energy offset a larger (LI_??) Historical output is sent resources. electrical load. insolation time to renewable This function series generators. is preferred (LI_04) Renewable where a Predicted generators are relatively insolation time not responsive small series to the amount of (LI_??) Historical transactive solar wind speed and control and renewable direction time coordination generation series system. offsets load (LI_05) at a location. Predicted wind If the energy speed and from a solar direction time energy series. resource (LI_15) Historical should affect relative humidity TIS at this time series and (LI_16) electrically Predicted downstream relative humidity locations, the time series. energy from (LI_17) Historical this resource air density time should be series incorporated (LI_18) with a Predicted air resource and density time incentive series toolkit (LI_19) Monthly function typical energy instead (See (LI_20) Total Table 29). nameplate or “typical” power capacity (LI_??) Predicted resource availability (LI_??) Solar Conversion Efficiency Curve 2.0 General Most general Applicable to Current IST time Predicted Event-Driven function for many series. inelastic load Demand predicting responsive Recent history at for each IST Response the asset systems (e.g., 1 day to 1 interval. behaviors of that conduct week) of TIS that Predicted responsive traditional may be used if change in load assets demand relative TIS is to elastic load for that only response be tracked in a each IST infrequently several times statistical sense. interval. respond. a month. (LI_01) Historical When these Response load time series assets may (LI_02) Actual respond they additionally measured load change define a TIS time series. between a “critical” (LI_??) Device very limited response count number of level for (LI_06) Day of available extreme week and response conditions. holidays levels. (LI_08) Daily It is load profile postulated (L1_28) Minimum that this event duration function can (LI_29) be designed Promised event flexibly to count or respond to frequency that absolute or has been relative TIS negotiated with as desired customers. by the (LI_30) application. Limitations on event duration that have been promised to Customers. 2.1 Represent Asset Current IST time Predicted Commercial especially systems such series. inelastic load Event-Driven the change as See 1.1 Bulk at for each IST Demand in elastic thermostats, Commercial interval. Response response water heaters, Loads. The Predicted from and HVACs. inputs that have change in commercial been defined for elastic load for entities that function 1.1 Bulk each IST are Commercial interval. performing Loads are again Predicted time lighting, used to predict series of space the inelastic load output advisory conditioning, component of control signals. or other the commercial See control of load to be SubAppendix commercial modeled by this C. (Default buildings. function. expects two Additionally, the load levels following inputs specified by may be used to the domain {0, model the 127}). The set change in elastic of output load: signals may be TIS time series. parametrically Recent history modified based (e.g., 1 day to 1 on the number week) of TIS that of available may be used if response relative TIS is to levels, a static be tracked in a input. statistical sense. (LI_??) Device Count (LI_29) Promised event count or frequency that has been negotiated with customers. (LI_30) Limitations on event duration that have been promised to customers. (LI_31) Representative unit changes in power that will occur at prescribed response levels. (LI_??) Number of response levels available from asset system. 2.2 Event- To be used Many Current IST time Predicted Driven where subproject series. inelastic load Distribution subprojects locations of (LI_01) Historical at for each IST System of the the load interval. Voltage Demonstration Demonstration TIS time series. Predicted Control have that (LI_32) Present change in offered to implement actual voltage elastic load for modulate conservation regulation level each IST distribution voltage Current IST time interval. system regulation series Predicted time voltage in (CVR) or (LI_35) series of response to voltage Implementer's output advisory relatively optimization criteria control signals. extreme and have concerning how See conditions of offered to often and how SubAppendix the TIS. This make system long voltage may C. (Default function voltage be affected at expects two should responsive to each level. Note load levels include the the TIS. that this input specified by option where may probably be the domain {0, the degree of adequately 127}). The set voltage represented by of output change is input types signals may be affected by LI_29 and LI_30. parametrically feedback (LI_36) Day-long modified based from hourly time on the number measurements series of relative of available of voltage fractions of load response at various that are constant levels, a static feeder impedance, input. locations. constant current, Regardless, and constant utilities power, should keep respectively customer (LI_??) Number voltage of response within levels available accepted from asset ranges. system. 2.4 Asset See 1.3 Bulk Predicted Residential systems. Residential inelastic load Event-Driven Load. The at for each IST Demand inelastic interval. Response residential load Predicted component may change in use the same elastic load for inputs as were each IST used for function interval 1.3 Bulk Predicted time Residential series of Load. output advisory The following control signals. additional inputs See may be used to SubAppendix predict changes C. (Default in the elastic expects two load component: load levels TIS time series specified by Current IST time the domain {0, series 127}). The set (LI_20) Total of output nameplate or signals may be “typical” power parametrically capability (of modified based devices to be on the number curtailed) of available (LI_??) Hourly response curtailable power levels, a static (LI_??) Device input. count (LI_28) Minimum Event Duration (LI_29) Promised Event Count or Frequency (LI_30) Limitations on Curtailment Event Duration (LI_31) Representative Changes in Power at Prescribed Response Levels (LI_??) Actual Number of Times that Actuation has Already Occurred in each Relevant Time Period (LI_??) Actual duration that actuation has already occurred in each relevant time period (LI_??) Number of response levels available from asset system. 2.5 Non- Asset (LI_01) Historical Predicted Renewable systems. Load or inelastic load Distributed Generation at for each IST Generation (LI_02) Actual interval. Event-Driven Measured Load Predicted Demand or Generation change in Response (LI_06) Day of elastic load for Week and each IST Holiday interval (LI_07) Average Predicted time Daily Load or series of Generation output advisory (LI_08) Daily control signals. Load or See Generation SubAppendix Profile C. (Default (LI_19) Monthly expects two Typical Energy load levels (LI_??) specified by Resource the domain {0, Schedule 127}). The set TIS time series of output (LI_??) Device signals may be Count parametrically (LI_20) Total modified based nameplate or on the number “typical” power of available capability (of response devices to be levels, a static curtailed) input. (LI_??) Hourly curtailable power (LI_??) Device count (LI_28) Minimum Event Duration (LI_29) Promised Event Count or Frequency (LI_30) Limitations on Curtailment Event Duration (LI_31) Representative Changes in Power at Prescribed Response Levels (LI_??) Actual Number of Times that Actuation has Already Occurred in each Relevant Time Period (LI_??) Actual duration that actuation has already occurred in each relevant time period (LI_??) Number of response levels available from asset system. 3.0 General Most general Applicable at See function 1.0 Predicted Time-of-Use function for locations that Bulk Inelastic inelastic load Demand predicting host simple Load. The inputs at for each IST Response responsive DR systems from 1.0 Bulk interval. load that should Inelastic Load Predicted behaviors of respond daily. are also useful change in groups of by this function elastic load for devices that for predicting the each IST respond to inelastic load interval. diurnal component. Predicted time variability in Additionally, the series of the TIS (e.g., following inputs output advisory respond to will be useful for control signals. one or more the prediction of See daily changes in SubAppendix intervals) elastic load C. (Default component: expects two TIS time series load levels (LI_??) Device specified by Count the domain {0, (LI_28) Minimum 127}). The set Event Duration of output (LI_29) signals may be Promised Event parametrically Count or modified based Frequency on the number (LI_30) of available Limitations on response Curtailment levels, a static Event Duration input. (LI_31) Representative Changes in Power at Prescribed Response Levels (LI_??) Actual Number of Times that Actuation has Already Occurred in each Relevant Time Period (LI_??) Actual duration that actuation has already occurred in each relevant time period (LI_??) Hourly Unit Expected Change in Power at Event Levels (LI_??) Number of response levels available from asset system. 3.1 Battery Represent Locations that (LI_01) Historical Predicted Storage- behaviors of host usually Load or inelastic load Time-of-Use battery small battery Generation at for each IST storage systems (LI_02) Actual interval. This systems that controlled Measured Load will normally are engaged simply on a or Generation be zero, with a daily diurnal (LI_20) Total assuming that pattern, pattern. Nameplate or the battery usually to Presently, no “Typical” Power charges and mitigate daily transactive Capacity discharges peak. Battery nodes claim (LI_??) Device only for is fully to be applying Count economic charging, battery (LI_28) Minimum reasons and fully systems in Event Duration according to discharging, this way. (LI_29) the condition of or resting. Promised the TIS signal. Maximum Event Predicted Count or change in Frequency elastic load for (LI_30) each IST Limitations on interval. Maximum Event Predicted time Duration series of (LI_31) output advisory Representative control signals. Changes in See Power at SubAppendix Prescribed C. (Default Response expects three Levels load levels (LI_??) Actual specified by Number of the domain {−127, Times that 0, 127}). Actuation has The set of Already output signals Occurred in may be each Relevant parametrically Time Period modified based (LI_??) Actual on the number duration that of available actuation has response already occurred levels, a static in each relevant input. time period (LI_41) Predicted Resource Fractional Availability Current IST time series. TIS time series. (LI_??) Battery state of charge. (LI_??) Useful Energy Storage Capacity (LI_??) Number of response levels available from asset system. 3.2 Represent Transactive See 1.1 Bulk Predicted Commercial effects of nodes that Commercial inelastic load Time-of-Use predominantly offer Loads. This at for each IST Demand commercial commercial function may use interval. Response lighting and system the same inputs Predicted space responses for as function 1.1. change in conditioning addressing Bulk Commercial elastic load for programs daily peak. Loads as it each IST that respond predicts the interval. to one or inelastic Predicted time several daily component of its series of peak load. output advisory periods. These additional control signals. inputs may be See used to calculate SubAppendix the change in C. (Default the elastic expects two component of load levels this function's specified by load: the domain {0, TIS time series. 127}). The set (LI_??) Device of output Count signals may be (LI_28) Minimum parametrically Event Duration modified based (LI_29) on the number Promised Event of available Count or response Frequency levels, a static (LI_30) input. Limitations on Curtailment Event Duration (LI_31) Representative Changes in Power at Prescribed Response Levels (LI_??) Actual Number of Times that Actuation has Already Occurred in each Relevant Time Period (LI_??) Actual duration that actuation has already occurred in each relevant time period (LI_??) Hourly Unit Expected Change in Power at Event Levels (LI_??) Number of response levels available from asset system. 3.4 Predict and Applied where See 1.3 Bulk Predicted Residential represent programmable, Residential inelastic load Time-of-Use response communicating Load. This at for each IST Demand from thermostats; function may use interval. Response automated smart the same inputs Predicted residential appliances, as for 1.3 Bulk change in demand- demand- Residential Load elastic load for response response to predict the each IST systems of switch units, inelastic interval. many types or other component of its Predicted time that will assets are load. series of respond installed in The following output advisory approximately residences additional inputs control signals. daily to and where may be used to See help mitigate programs are predict the SubAppendix peak designed to change in elastic C. (Default conditions. have these load: expects two This function systems TIS time series. load levels applied to respond to (LI_??) Device specified by automated daily peak Count the domain {0, responses periods. (LI_28) Minimum 127}). The set and may Asset Event Duration of output accommodate systems such (LI_29) signals may be customer as water Promised Event parametrically opt-out. heater control, Count or modified based thermostat Frequency on the number load control. (LI_30) of available Limitations on response Curtailment levels, a static Event Duration input. (LI_31) Representative Changes in Power at Prescribed Response Levels (LI_??) Actual Number of Times that Actuation has Already Occurred in each Relevant Time Period (LI_??) Actual duration that actuation has already occurred in each relevant time period (LI_??) Hourly Unit Expected Change in Power at Event Levels (LI_??) Number of response levels available from asset system. 3.5 Time-of- Similar to Applicable Current IST time Predicted Use toolkit where voltage series. inelastic load Distribution function 2.2, is controlled Historical power at for each IST System except at two or more consumption interval. Voltage voltage may levels TIS time series. Predicted Control be controlled according to TIS threshold(s), change in according to the value of which may elastic load for daily on- and the TIS and further be each IST off-peak other inputs parametrically interval. periods. and where affected. Predicted time responses of (LI_??) Number series of the asset of response output advisory have been levels available control signals. designed to from asset See occur system. SubAppendix according to C. (Default daily on-and expects two off-peak load levels periods. specified by the domain {0, 127}). The set of output signals may be parametrically modified based on the number of available response levels, a static input. 3.6 Time-of- Asset See 3.1 Battery Predicted Use Electric systems such Storage-Time- inelastic load Vehicle as vehicle of-Use. This at for each IST Charging charging. function is interval. expected to use Predicted the same inputs change in as does 3.1 elastic load for Battery each IST Storage-Time- interval of-Use. Predicted time Additionally, series of these inputs may output advisory be used control signals. because of the See special SubAppendix characteristics of C. (Default electric vehicles: expects two (LI_??) Time at load levels Which Energy specified by Storage Should the domain {0, be Fully 127}). The set Charged of output (LI_??) Number signals may be of response parametrically levels available modified based from asset on the number system. of available response levels, a static input. 3.7 Non- This function Asset Maximum Predicted Renewable predicts the systems. allowed rate of inelastic load Distributed response change in (generation) Generation from a non- generated power from this asset Time-of-Use renewable Number of system Demand distributed response levels Predicted Response generator to be prescribed average demand- for this asset change in response system elastic load for system that Typical fraction each IST will respond of time that each interval approximately response level/ Predicted time daily to should be active series of help mitigate during a day output advisory peak Minimum time control signals. conditions duration for See that are which an event SubAppendix evident in an level/should C. (Default incentive remain in force expects two signal. for this day type load levels after it has specified by become initiated the domain {0, Maximum total 127}). The set event duration of output permitted per signals may be day type and per parametrically event allowed for modified based each event on the number level/ of available Limitations on response the minimum levels, a static number of TOU input. events that may be called during the three major day types for each response level/ Limitations on the maximum number of TOU events that may be called during the three major day types for each response level/ Recent history of TIS Current TIS for future IST intervals Typical baseline power that is generated during UTC hour h of a weekday day type by this distributed generation resource Typical baseline power that is generated during hour h of a weekend day by this distributed generation resource Change in generation that may be anticipated at each of the L response levels 4.0 General Most general Applicable at Current IST time Predicted Real-time function for locations that series. inelastic load Continuum predicting host simple Historical power at for each IST Demand responsive RT systems. consumption interval. Response load TIS time series. Predicted behaviors of Parametric change in groups of algorithm by elastic load for devices that which change in each IST respond elastic load may interval. according to be predicted. Predicted time a continuum series of of possible output advisory responses. control signals. See SubAppendix C. (Default expects a continuum of advisory levels [0, 127]). 4.1 Battery Predict and Applicable to Current IST time Predicted Storage- represent the battery series. inelastic load Real-Time response storage Historical power at for each IST and systems that consumption, interval. condition of respond very generation Predicted a battery dynamically to patterns change in system is the TIS and TIS time series. elastic load for highly other local Parametric each IST responsive conditions algorithm by interval. to the and provide which change in Predicted time dynamic also a elastic load may series of changes in continuum of be predicted. output advisory the TIS and charge and State of charge. control signals. that discharge Limitations on See responds levels. maximum SubAppendix using a Asset charge and C. (Default continuum of systems such discharge levels. expects a charge and as Demand continuum of discharge Shifters and advisory levels levels. distribution [−127, 127]). batteries. 4.2 Predict and Mostly Current IST time Predicted Commercial represent applicable to series. inelastic load Real-Time dynamic commercial Historical power at for each IST Demand commercial space heating consumption interval. Response demand- but may be TIS time series Predicted response applicable to Parametric change in systems that other algorithm by elastic load for observe the commercial which change in each IST full dynamics devices that elastic load may interval. of the TIS observe the be predicted Predicted time (and other full dynamics series of information) of the TIS output advisory and (and other control signals. dynamically information) See respond and respond SubAppendix using a with a C. (Default continuum of continuum of expects a possible possible continuum of control control advisory levels outcomes. outcomes [0, 127]). (e.g., temperature settings). 4.3 Real- Asset Predicted Time systems. inelastic load Distribution at for each IST System interval. Voltage Predicted Control change in elastic load for each IST interval Predicted time series of output advisory control signals. See SubAppendix C. (Default expects a continuum of advisory levels [0, 127]). 4.5 Predict and Applicable Current IST time Predicted Residential represent where series. inelastic load Real-Time responses residential Historical power at for each IST Demand from the customers consumption interval. Response most possess TIS time series Predicted dynamic of space Parametric change in residential conditioning algorithm by elastic load for demand- systems that which change in each IST response observe the elastic load may interval. system that dynamics of be predicted Predicted time observe the the TIS and Day and time of series of dynamics of provide a day output advisory the TIS (and continuum of control signals. other responses. See information) Asset SubAppendix and systems. C. (Default automatically expects a respond with continuum of any of a advisory levels continuum of [0, 127]). possible responses. 5.0 General (LI_??) Number Predicted Manual or of response inelastic load Behavioral levels available at for each IST Demand from asset interval. Response system. Predicted change in elastic load for each IST interval. Predicted time series of output advisory control signals. See SubAppendix C. (Default expects a continuum of advisory levels [0, 127]). 5.1 Special case Applicable Current IST time Predicted Residential of toolkit load where series. inelastic load Behavioral function 5.0 residential Prediction of the at for each IST Response to where the customers inelastic load interval. Portals or In- means of have been output may use Predicted Home conveying provided in- the same inputs change in Displays demand- home displays as were elastic load for response or portals that described for each IST information display the function 1.0 Bulk interval. or requests TIS. Inelastic Load. Variant #1- to residents Asset Refer to that continuum: is either an systems. function. Where Current TIS in-home the load is signal is display or predominantly relayed to the energy residential, portal or in- portal. An commercial, or home display. energy portal industrial, the Variant #2- or in-home- designer should discrete levels: display is a refer to the Predicted time dedicated respective series of piece of functions 1.1, output advisory equipment 1.2 or 1.3. control signals for the The following are sent to in- conveyance additional inputs home display of demand- are used to or portal that response predict the convey information change in elastic discrete or advice. load: response The TIS time series. levels for actuation of (LI_??) Number events or time energy of response of use periods. responses is levels available See not from asset SubAppendix automated system. C. (Default by this expects two function, but load levels the means specified by by which the the domain {0, customer is 127}). The set informed or of output advised signals may be should be parametrically automated. modified based on the number of available response levels, a static input. 5.2 Predict and Locations Current IST time Predicted Residential represent where series. inelastic load Behavioral elastic humans are (LI_??) Number at for each IST Response - response informed of response interval. No Portals or from assets about extreme levels available Predicted In-Home that both use power grid from asset change in Displays human events and system. elastic load for decisions are invited to each IST and action take actions interval. but do not that would Variant #1- use energy mitigate the continuum: portals or in- events. Current TIS home signal is displays to relayed to the convey portal or in- demand- home display. response Variant #2- information discrete levels: or requests. Predicted time series of output advisory control signals are sent to in- home display or portal that convey discrete response levels for events or time of use periods. See SubAppendix C. (Default expects two load levels specified by the domain {0, 127}). The set of output signals may be parametrically modified based on the number of available response levels, a static input. 5.3 Manual Asset (LI_??) Number Predicted Commercial Systems. of response inelastic load Demand levels available at for each IST Response from asset interval. system. Predicted change in elastic load for each IST interval. Predicted time series of output advisory control signals. See SubAppendix C. (Default expects two load levels specified by the domain {0, 127}). The set of output signals may be parametrically modified based on the number of available response levels, a static input. 5.4 Manual Predictive Asset Current IST time Predicted Non- advisory systems. series. inelastic load Renewable signals TIS time series. (generation) at Distributed should be (LI_37) for each IST Energy formulated Frequency or interval. Resources and number of times Predicted Demand conveyed to that the DER change in Response operations may be elastic load personnel at actuated. Note: (generation) Lower Valley this input should for each IST and be replaced by interval. University of more general Variant #1- Washington. LI_29. continuum: The (LI_29) Current TIS operations Promised event signal is people will count or relayed to the then frequency that portal or in- manually have been home display. schedule negotiated with Variant #2- and/or customer discrete levels: control their (LI_??) Number Predicted time distributed of times that series of generation actuation has output advisory Resources already occurred control signals correspondingly. in each relevant are sent to in- time period. home display (LI_??) Actual or portal that duration that convey actuation has discrete already occurred response in each relevant levels for time period. events or time- Note: Should of-use periods. replace this input See with more SubAppendix general LI_30. C. (Default (LI_30) expects two Limitations on load levels curtailment specified by event duration the domain {0, that have been 127}). The set promised to of output customer signals may be Note: this input parametrically should be modified based replaced by the on the number more general of available LI_30. response Note: This levels, a static should be input. replaced by LI_20, which shares the same meaning. (LI_20) Total Nameplate or “Typical” Power Capacity. (LI_41) Limitations on operator ability to receive and schedule responses. (LI_??) Number of response levels available from asset system.

6.3 Appendix C Collected Set of Example Toolkit Functions

This section introduces a variety of exemplary load and incentive functions, any one or more of which can be used in embodiments of the disclosed technology (e.g., in a toolkit library). The functions described below should not be construed as limiting in any way, and are example implementations of functions that can be used in a transactive control and coordination system. Further, the equations, tables, and subappendices in the function descriptions below will have their own independent numbering and labeling conventions. Still further, in some instances, some information may be omitted from certain functions but could be implemented by those skilled in the art.

6.3.1 Bulk Inelastic Load N-Day Moving Window (Function 1.01) Description

The following is the foundation of an alternative toolkit function to 1.0 Bulk Inelastic Load. However, this functional specification can be implemented with initial measurements over only two prior days, expects less mathematical knowledge by implementers, is easily documented down to requisite steps, and, for these reasons, may be more amenable to implementation by some utility implementers.

The basic approach is as follows: For a given circuit location, pairs of electrical load and ambient temperature are measured each hour. Data from the same hour-of-day and from a comparable day type, for a window of a chosen number of days, are used to compute the coefficients of a linear model. This model is then used to predict electrical load at this location for the same future day type and hour-of-day based on the forecasted ambient temperature for the future hour.

Block Input/Output Function Model: Inputs:

-   -   {P_(d,h), T_(d,h)}—[kW, ° C.]—paired measurements of actual         electrical power (load) and ambient temperature for a given day         d of a given type (weekday or weekend/holiday) and hour h of the         day at a circuit location. h=0, 1, . . . , 23. These         measurements taken each hour allow the recursive model to become         updated for the respective day type and hour-of-day.     -   N—[dimensionless]—number of days in the moving window that will         be used in the model formulation. Default: 10 (e.g., about two         weeks of weekdays or about a month of weekend/holiday days).     -   T_(f) _(—) _(d,h)—[° C.]—forecasted temperature for a given         future hour-of-day h for a least the next four days (e.g., the         predicted time horizon of the transactive signals). This         forecasted temperature is the input to the model by which         electrical power load may be predicted for a given hour-of-day         and day type.

Interim Calculation Products:

-   -   a₀ _(—) _(h), a₁ _(—) _(h)—[kW, kW/° C.]—a set of coefficients         that model a best-fit prediction of electrical power from a         forecasted ambient temperature for a given hour-of-day on a         given type of day.     -   A₀₀ _(—) _(h), A₀₁ _(—) _(h), A₁₁ _(—) _(h), b₀ _(—) _(h), b₁         _(—) _(h)—set of five unique vector and matrix elements that         should be stored for each hour-of-day for each day type. These         elements are updated each time a new pair of load and         temperature measurements become available for the respective         hour-of-day and day type.     -   {circumflex over (P)}_(d,h)—[kW]—predicted load for each future         hour for the next four days. These are the outputs from the         linear model for the respective future hour-of-day and day type,         given the forecasted ambient temperature for that future hour.

Outputs:

-   -   L_(inelastic) _(—) _(n)—[kW]—predicted load corresponding to the         n^(th) interval. This is the hourly predicted load {circumflex         over (P)}_(d,h) allocated accordingly to each n^(th) interval.

Pseudo Code Implementation:

-   -   1. For d available measurements, calculate A₀₀ _(—) _(h), A₀₁         _(—) _(h), A₁₁ _(—) _(h), b₀ _(—) _(h), and b₁ _(—) _(h). At         startup, two measurements (e.g., d=2) may be adequate. More         prior measurements are preferred and may be used. It should be         pointed out that singularity is unavoidable when d=1; the         determinant of matrix A, as derived in Appendix A, is zero.

$\begin{matrix} {{\forall h},\begin{matrix} {A_{00{\_ h}} = {\min \left( {d,N} \right)}} \\ {A_{01{\_ h}} = {\sum\limits_{i = {\max {({1,{d - N + 1}})}}}^{d}T_{i,h}}} \\ {A_{11{\_ h}} = {\sum\limits_{i = {\max {({1,{d - N + 1}})}}}^{d}T_{i,h}^{2}}} \\ {b_{0{\_ h}} = {\sum\limits_{i = {\max {({1,{d - N + 1}})}}}^{d}P_{i,h}}} \\ {b_{1{\_ h}} = {\sum\limits_{i = {\max {({1,{d - N + 1}})}}}^{d}\left( {P_{i,h} \cdot T_{i,h}} \right)}} \end{matrix},{d \geq 2}} & (1) \end{matrix}$

-   -   Note that singularity will still occur for d>1 if T_(i,h) are         identical for a given h.     -   This example method uses at most N×24×2 data points, which are         stored for each day type.     -   At the implementer's discretion, equation 2 may be employed         instead of equation 1. Equation 2 modestly reduces computations.         However, equation 2 uses additional data that is stored (e.g.,         N×24×7 compared to N×24×2).

$\begin{matrix} {{\forall h},\begin{matrix} {A_{00{\_ h}} = {\min \left( {d,N} \right)}} \\ {A_{01{\_ h}} = \left\{ \begin{matrix} {{A_{01{\_ h}}^{*} + T_{d,h}},} & {{{if}\mspace{14mu} d} \leq N} \\ {{A_{01{\_ h}}^{*} - T_{{d - N},h} + T_{d,h} + T_{d,h}},} & {otherwise} \end{matrix} \right.} \\ {A_{11{\_ h}} = \left\{ \begin{matrix} {{A_{01{\_ h}}^{*} + T_{d,h}},} & {{{if}\mspace{14mu} d} \leq N} \\ {{A_{11{\_ h}}^{*} - T_{{d - N},h}^{2} + T_{d,h}^{2}},} & {otherwise} \end{matrix} \right.} \\ {b_{0{\_ h}} = \left\{ \begin{matrix} {{b_{0{\_ h}}^{*} + P_{d,h}},} & {{{if}\mspace{14mu} d} \leq N} \\ {{b_{0{\_ h}}^{*} - P_{{d - N},h} + P_{d,h}},} & {otherwise} \end{matrix} \right.} \\ {b_{1{\_ h}} = \left\{ \begin{matrix} {{b_{1{\_ h}}^{*} + {P_{d,h} \cdot T_{d,h}}},} & {{{if}\mspace{14mu} d} \leq N} \\ {{b_{1{\_ h}}^{*} - {P_{{d - N},h} \cdot T_{{d - N},h}} + {P_{d,h} \cdot T_{d,h}}},} & {otherwise} \end{matrix} \right.} \end{matrix}} & (2) \end{matrix}$

-   -   -   A*₀₁, A*₁₁, b*₀, and b*₁ are A₀₁, A₁₁, b₀, and b₁ from the             preceding iteration, respectively.

    -   2. After matrix and vector elements have been calculated by         either equation 1 or equation 2, calculate the coefficients for         the linear model using equation 3.

$\begin{matrix} {{\forall h},\begin{matrix} {a_{0{\_ h}} = \frac{{A_{11{\_ h}}b_{0{\_ h}}} - {A_{01{\_ h}}b_{1{\_ h}}}}{{A_{00{\_ h}}A_{11{\_ h}}} - A_{01{\_ h}}^{2}}} \\ {a_{1{\_ h}} = \frac{{A_{00{\_ h}}b_{1{\_ h}}} - {A_{01{\_ h}}b_{0{\_ h}}}}{{A_{00{\_ h}}A_{11{\_ h}}} - A_{02{\_ h}}^{2}}} \end{matrix}} & (3) \end{matrix}$

-   -   3. Generate {circumflex over (P)} for the upcoming four days         using the linear model in equation 4:

for D={d+1,d+2,d+3,d+4}, and ∀h,{circumflex over (P)} _(D,h) =a ₀ _(—) _(h) +a ₁ _(—) _(h) ·T _(f) _(—) _(D,h)  (4)

-   -   The hourly standard deviation σ_(h), which is potentially a         useful indicator of the accuracy of and one's confidence in the         hourly prediction {circumflex over (P)}_(D,h), may be computed         as follows:

$\begin{matrix} {{\forall h},\begin{matrix} {\sigma_{h} = \sqrt{\frac{1}{\min \left( {d,N} \right)}{\sum\limits_{i = {\max {({1,{d - N + 1}})}}}^{d}\left( {P_{i,k} - {\hat{P}}_{i,h}} \right)^{2}}}} \\ {= \sqrt{\frac{1}{\min \left( {d,N} \right)}{\sum\limits_{i = {\max {({1,{d - N + 1}})}}}^{d}\left( {P_{i,h} - \left( {a_{0{\_ h}} + {a_{1{\_ h}} \cdot T_{i,h}}} \right)} \right)^{2}}}} \end{matrix}} & (5) \end{matrix}$

-   -   4. Generate L_(inelastic) _(—) _(n) by allocating {circumflex         over (P)}_(D,h) to each n^(th) interval:

$\begin{matrix} {{\forall n},{L_{inelastic\_ n} = \left\{ \begin{matrix} {{\hat{P}}_{D,h},} & {{{if}\mspace{14mu} n} \subseteq h} \\ \overset{\_}{{\hat{P}}_{D,h},} & {{{if}\mspace{14mu} h} \subseteq n} \end{matrix} \right.}} & (6) \end{matrix}$

-   -   -   {circumflex over (P)}_(D,h) is the average of all             {circumflex over (P)}_(D,h) corresponding to all hours h             lying within n.

    -   Make this L_(inelastic) _(—) _(n) prediction available as an         output of this function into the transactive node's algorithmic         toolkit framework.

    -   5. Each time a successive measurement pair becomes available,         repeat starting from step 1 above.

Subappendix A Additional Details about the Formulation

This formulation is based on a first-order polynomial (linear) model of power {circumflex over (P)} as a function of temperature T, as shown in equation A1. This model's coefficients a₀, and a₁ are determined via a least-squares error fit to pairs of measured power and temperature. The coefficients may be used thereafter to predict power given forecasted temperatures.

{circumflex over (P)}=a ₀ +a ₁ ·T  (A1)

The optimal coefficients are determined by minimization of the cost function J shown in equation A2. This wisely chosen cost function happens to be the statistical variance of the difference between actual measured electrical load and load that is modeled by the linear model during N days of a given type (weekdays, or weekends/holidays). The standard deviation is the square root of the variance. The variance and standard deviation are potentially useful indicators of the accuracy of and one's confidence in the predictions that result from this function.

$\begin{matrix} {J = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {P_{i} - {\hat{P}}_{i}} \right)^{2}}}} & ({A2}) \end{matrix}$

The optimal coefficients are found by setting the partial derivatives of the cost function with respect to the two coefficients to zero, as shown in equation A3.

$\begin{matrix} {\begin{bmatrix} \frac{\partial J}{\partial a_{0}} \\ \frac{\partial J}{\partial a_{1}} \end{bmatrix} = {\begin{bmatrix} {{- \frac{2}{N}}{\sum\limits_{i = 1}^{N}\left( {P_{i} - a_{0} - {a_{1} \cdot T_{i}}} \right)}} \\ {{- \frac{2}{N}}{\sum\limits_{i = 1}^{N}\left( {{P_{i} \cdot T_{i}} - {a_{0} \cdot T_{i}} - {a_{1} \cdot T_{i}^{2}}} \right)}} \end{bmatrix} = 0}} & ({A3}) \end{matrix}$

Equation A3 can be written in matrix form, as in equation A4.

$\begin{matrix} {{\begin{bmatrix} N & {\sum\limits_{i = 1}^{N}T_{i}} \\ {\sum\limits_{i = 1}^{N}T_{i}} & {\sum\limits_{i = 1}^{N}T_{i}^{2}} \end{bmatrix}\begin{bmatrix} a_{0} \\ a_{1} \end{bmatrix}} = \begin{bmatrix} {\sum\limits_{i = 1}^{N}P_{i}} \\ {\sum\limits_{i = 1}^{N}\left( {P_{i} \cdot T_{i}} \right)} \end{bmatrix}} & ({A4}) \end{matrix}$

The matrix is seen to be identical to its transpose. The simplified representation given in equation A5 will prove useful in referring to the various vector and matrix elements of equation A4.

$\begin{matrix} {{\begin{bmatrix} A_{00} & A_{01} \\ A_{01} & A_{11} \end{bmatrix}\begin{bmatrix} a_{0} \\ a_{1} \end{bmatrix}} = \begin{bmatrix} b_{0} \\ b_{1} \end{bmatrix}} & ({A5}) \end{matrix}$

This is in the form Ax=b, the solution of which can be found by x=A⁻¹b, as long as matrix A is invertible or nonsingular. Formulas exist for the inversion of a 2×2 matrix, so each coefficient may be explicitly solved for as in equation A6. This explicit representation is advantageous because it alleviates any expectation that the computational infrastructure being relied upon to conduct this function necessarily possesses any matrix solvers.

$\begin{matrix} {{a_{0} = \frac{{A_{11}b_{0}} - {A_{01}b_{1}}}{{A_{00}A_{11}} - A_{01}^{2}}}{a_{1} = \frac{{A_{00}b_{1}} - {A_{01}b_{0}}}{{A_{00}A_{11}} - A_{01}^{2}}}} & ({A6}) \end{matrix}$

This method should not require a large set of training data, but some startup issues may be encountered. There is no reasonable way to predict electrical load before any comparable measurement has been made. The coefficients cannot be uniquely determined until at least two non-identical temperature measurements have been taken for a given hour of the day.

Subappendix B Example

In this example, real power (load) P and temperature T measurements during fourteen weekdays—given in Table 31 and Table 32, respectively—are used to compute {circumflex over (P)}, following the procedure outlined in the Pseudo Code Implementation section. N=10. The resulting {circumflex over (P)} is given in Table 33, and plotted along with ±1 standard deviation (e.g. ±√{square root over (J)}) and P in the set 4700 of graphs shown in FIG. 47. Notice that the “NaN” (not a number) entries on day 3 are due to the singularity of matrix A caused by the identical temperature points at the corresponding hours on days 1 and 2. FIG. 48 through FIG. 50 comprise sets 4800, 4900, 5000 of graphs that show the linear least-squares error fit for each hour of the day, for days 4, 12, and 14, respectively, given the measured data.

TABLE 31 Power P Measurements in kW d 1 2 3 4 5 6 7 8 9 10 11 12 13 14 h 0 126630 126380 123750 119310 108010  91850 101540  99580 110370 118090 111810 108690  94420  99760 1 128540 127530 126080 119370 106720  90490 101250  99270 110440 115540 112920 107110  92590  99970 2 130030 132390 128840 118230 107120  90680 102500  99460 112350 115970 114350 106530  92940 101600 3 132300 134530 129970 119680 109430  92310 104400 100900 116400 117040 118210 108160  93430 103750 4 136720 137780 131020 120650 110730  93910 105960 102830 120110 117440 121380 108240  95000 106250 5 141660 143280 135970 122840 113740  96180 110190 107220 126350 120040 127690 112090  99450 111410 6 151840 151040 144810 131840 121820 105230 119760 117030 135810 127720 135390 120230 108640 120590 7 164120 161680 157710 142160 132860 114240 130250 129380 146520 138180 145470 129690 116230 131540 8 166680 162390 158210 142940 134880 116610 131660 126770 151070 140760 150230 130020 115310 131170 9 158610 156650 150760 137720 132790 116310 126940 121170 146550 138550 145140 127470 112020 121080 10 150280 145960 144010 131050 130430 107040 119110 113870 137590 135270 135700 123850 107310 114660 11 140770 138850 138650 120960 124670 100140 114120 107110 128370 128050 128430 120340 104290 108770 12 132130 130430 134000 110740 120430  96160 111270 101900 120040 116560 122470 115930 103010 105390 13 125840 125450 131130 105590 115060  96720 103900  97780 113440 109900 115470 114020 103600 103400 14 120530 119940 130460 102400 114400  93370 102900  94950 110830 106170 114590 114710 105380 101570 15 118960 117000 129940 102900 111120  94600 101420  92960 109080 102160 117490 116450 106720 102620 16 116740 116360 131310 103930 111810  94570 102470  94420 109880 102600 118930 117110 110980 103650 17 123890 121190 135200 107620 117810 102710 108120  99210 115810 108130 125210 119550 114430 111170 18 137920 135820 141200 118540 125000 110720 119310 111320 128410 120590 131300 126230 121330 118390 19 142510 139340 141880 122890 123340 114190 121300 118170 132660 124750 133520 126760 121940 123540 20 142980 138900 139110 122620 119860 114130 118760 119720 134420 126250 128930 122130 116690 122810 21 142550 137650 135470 122060 117290 111990 115170 117520 131880 124860 125790 116520 111650 120670 22 136270 133130 130030 117390 112710 107870 109270 114110 128100 121910 120550 107950 110480 114810 23 129740 126930 121660 112760 106290 103520 102800 111150 121650 117880 111880  99490 100620 107230

TABLE 32 Temperature T Measurements in ° C. d 1 2 3 4 5 6 7 8 9 10 11 12 13 14 h 0 −21.00 −22.00 −21.00 −14.00 −14.00 −4.00 −12.00 −10.00 −16.00 −17.00 −22.00 −7.00 3.00 −3.00 1 −22.00 −22.00 −21.00 −14.00 −13.00 −4.00 −11.00 −9.00 −17.00 −14.00 −21.00 −7.00 3.00 −4.00 2 −22.00 −23.00 −21.00 −13.00 −13.00 −4.00 −12.00 −9.00 −17.00 −14.00 −21.00 −6.00 2.00 −6.00 3 −23.00 −23.00 −19.00 −12.00 −12.00 −4.00 −13.00 −8.00 −23.00 −12.00 −21.00 −7.00 3.00 −6.00 4 −22.00 −22.00 −20.00 −12.00 −10.00 −5.00 −11.00 −8.00 −20.00 −11.00 −21.00 −6.00 2.00 −6.00 5 −23.00 −24.00 −20.00 −12.00 −10.00 −5.00 −10.00 −8.00 −22.00 −11.00 −19.00 −6.00 3.00 −7.00 6 −23.00 −24.00 −20.00 −12.00 −11.00 −8.00 −9.00 −8.00 −23.00 −11.00 −19.00 −5.00 3.00 −7.00 7 −24.00 −23.00 −20.00 −11.00 −10.00 −7.00 −8.00 −9.00 −22.00 −11.00 −19.00 −4.00 3.00 −7.00 8 −23.00 −22.00 −18.00 −10.00 −9.00 −9.00 −8.00 −13.00 −22.00 −11.00 −21.00 −4.00 3.00 −7.00 9 −19.00 −19.00 −16.00 −9.00 −9.00 −8.00 −8.00 −12.00 −20.00 −10.00 −18.00 −3.00 3.00 −6.00 10 −17.00 −16.00 −14.00 −8.00 −8.00 −7.00 −7.00 −9.00 −16.00 −8.00 −16.00 −2.00 4.00 −5.00 11 −16.00 −14.00 −13.00 −4.00 −8.00 −5.00 −2.00 −9.00 −15.00 −8.00 −13.00 −2.00 1.00 −5.00 12 −13.00 −13.00 −11.00 −4.00 −7.00 −3.00 −2.00 −6.00 −13.00 −8.00 −9.00 −2.00 3.00 −5.00 13 −12.00 −11.00 −10.00 −4.00 −6.00 −2.00 −1.00 −6.00 −12.00 −5.00 −7.00 −2.00 2.00 −4.00 14 −11.00 −8.00 −9.00 −4.00 −6.00 −1.00 −2.00 −4.00 −11.00 −4.00 −5.00 −1.00 2.00 −4.00 15 −11.00 −7.00 −9.00 −4.00 −5.00 −1.00 −2.00 −4.00 −10.00 −4.00 −6.00 −1.00 3.00 −4.00 16 −12.00 −7.00 −9.00 −5.00 −4.00 1.00 −3.00 −5.00 −7.00 −5.00 −6.00 −1.00 3.00 −3.00 17 −11.00 −8.00 −9.00 −3.00 −3.00 −1.00 −3.00 −6.00 −9.00 −4.00 −6.00 0.00 3.00 −4.00 18 −16.00 −9.00 −9.00 −8.00 −4.00 −2.00 −4.00 −7.00 −11.00 −13.00 −6.00 −1.00 3.00 −5.00 19 −18.00 −12.00 −11.00 −8.00 −4.00 −2.00 −7.00 −13.00 −16.00 −11.00 −6.00 0.00 3.00 −6.00 20 −19.00 −19.00 −12.00 −11.00 −5.00 −5.00 −6.00 −12.00 −16.00 −19.00 −6.00 0.00 3.00 −6.00 21 −19.00 −19.00 −12.00 −12.00 −5.00 −5.00 −6.00 −10.00 −16.00 −20.00 −7.00 1.00 0.00 −7.00 22 −21.00 −19.00 −15.00 −13.00 −4.00 −11.00 −10.00 −12.00 −18.00 −18.00 −7.00 2.00 −3.00 −7.00 23 −18.00 −19.00 −14.00 −15.00 −4.00 −11.00 −11.00 −17.00 −17.00 −20.00 −6.00 2.00 −3.00 −7.00

TABLE 33 Predicted Load {circumflex over (P)} in kW d 1 2 3 4 5 6 7 8 9 10 11 12 13 14 h 0 — — 126630 116860 119280 97461 108369 103079 114703 116243 126716 97364 87557 98185 1 — — NaN 112395 118233 95376 106179 100882 117808 110548 125738 97212 84840 98109 2 — — 127670 114445 118140 94987 109251 101292 119049 111531 127500 95262 86552 100584 3 — — NaN 123941 120102 101061 114440 101943 134691 109605 126623 101920 90763 102800 4 — — NaN 106100 116988 103511 112066 103657 132651 110065 132656 101073 90988 104233 5 — — 136800 121254 119314 106297 112426 107226 140265 113660 131096 104291 91067 109562 6 — — 154240 131262 130109 119518 115470 114200 151411 121797 139185 110797 101535 118367 7 — — 154360 143738 140592 130533 125652 129383 161018 133619 151356 119835 111943 128784 8 — — 145230 145832 141494 138080 128147 141862 163310 134415 157896 121255 114016 129418 9 — — NaN 134730 137518 133002 126763 138324 159603 130209 150645 116328 112387 125733 10 — — 137320 131948 131114 128706 120439 126313 147527 121093 145033 109665 106991 120504 11 — — 137890 131747 128323 121719 105742 125792 137670 120420 131670 109383 108827 115807 12 — — NaN 143520 119083 110190 100484 115130 132834 117456 119772 103365 97644 111728 13 — — 125060 145988 112810 102765 96465 113238 128199 107849 112711 101571 97728 107297 14 — — 120137 126527 111990 97488 98285 106113 128043 104100 106987 97088 95781 106263 15 — — 117980 119517 109447 99199 99613 106146 123478 103701 108810 95299 92736 105769 16 — — 116512 122828 108659 101560 105818 109718 112495 107500 109352 95750 92437 106631 17 — — 122090 126991 109571 109435 111212 118081 122865 110506 114634 101191 102691 111875 18 — — 135820 138594 125970 123013 120876 126128 131917 134944 121585 117003 117667 121233 19 — — 138812 139867 123367 120126 126652 137312 138238 129727 122264 118178 120110 123841 20 — — NaN 138849 120765 120152 119458 129805 135371 140289 118805 114907 116592 121461 21 — — NaN 135470 117430 117330 116924 123902 134394 141283 117843 110225 114585 118739 22 — — 126850 127799 102646 120991 116588 118679 128845 128699 109237 101337 110449 113300 23 — — 140980 123450 94357 115177 112747 121624 119604 124703 103275 98270 104107 106232

6.3.2 Bulk Inelastic Load Recursive—Recursive Algorithm (Function 1.01a) Description

The following is the foundation of an alternative to the Bulk Inelastic Load toolkit functions 1.0 and 1.01. However, this functional specification can be implemented with measurements over only two prior days, expects less mathematical knowledge by implementers, is easily documented down to requisite steps, and for, these reasons, may be more amenable to implementation by some utility implementers. Furthermore, unlike toolkit function 1.01 that uses a moving window of a chosen number of days, this function 1.01a is formulated as a purely recursive algorithm.

The basic approach is as follows: For a given circuit location, pairs of electrical load and ambient temperature are measured each hour. Data from the same hour-of-day and from a comparable day type are used to recursively update the coefficients of a linear model. This model is then used to predict electrical load at this location for the same future day type and hour-of-day based on the forecasted ambient temperature for the future hour.

Block Input/Output Function Model: Inputs:

-   -   {P_(d,h), T_(d,h)}—[kW, ° C.]—paired measurements of actual         electrical power (load) and ambient temperature for a given day         d of a given type (weekday or weekend/holiday) and hour h of the         day at a circuit location. h=0, 1, . . . , 23. These         measurements taken each hour allow the recursive model to become         updated for the respective day type and hour-of-day.     -   N—[dimensionless]—number used in the recursive algorithm. The         selected value of N should be greater than 2. Default: 10 (e.g.,         about two weeks of weekdays or about a month of weekend/holiday         days).     -   T_(f) _(—) _(d,h)—[° C.]—forecasted temperature for a given         future hour-of-day h for a least the next four days (e.g., the         predicted time horizon of the transactive signals). This         forecasted temperature is the input to the model by which         electrical power load may be predicted for a given hour-of-day         and day type.

Interim Calculation Products:

-   -   a₀ _(—) _(h), a₁ _(—) _(h)—[kW, kW/° C.]—a set of coefficients         that model a best-fit prediction of electrical power from a         forecasted ambient temperature for a given hour-of-day on a         given type of day.     -   A₀₁ _(—) _(h), A₁₁ _(—) _(h), b₀ _(—) _(h), b₁ _(—) _(h)—set of         four unique vector and matrix elements that should be stored for         each hour-of-day for each day type. These elements are updated         each time a new pair of load and temperature measurements become         available for the respective hour-of-day and day type.     -   {circumflex over (P)}_(d,h)—[kW]—predicted load for each future         hour for the next four days. These are the outputs from the         linear model for the respective future hour-of-day and day type,         given the forecasted ambient temperature for that future hour.

Outputs:

-   -   L_(inelastic) _(—) _(n)—[kW]—predicted load corresponding to the         n^(th) interval. This is the hourly predicted load {circumflex         over (P)}_(d,h) allocated accordingly to each n^(th) interval.

Pseudo Code Implementation:

-   -   1. For the number m of available startup measurements, calculate         the initial A₀₁ _(—) _(h), A₁₁ _(—) _(h), b₀ _(—) _(h), and b₁         _(—) _(h). At startup, two unique measurements (e.g., m=2) may         be adequate. More prior measurements are preferred and may be         used. It should be pointed out that singularity is unavoidable         when m=1; the determinant of matrix A, as derived in Appendix A,         is zero.

$\begin{matrix} {{\forall h},\begin{matrix} {A_{01{\_ h}} = {\frac{1}{m}{\sum\limits_{i = 1}^{m}T_{i,h}}}} \\ {A_{11{\_ h}} = {\frac{1}{m}{\sum\limits_{i = 1}^{m}T_{i,h}^{2}}}} \\ {b_{0{\_ h}} = {\frac{1}{m}{\sum\limits_{i = 1}^{m}P_{i,h}}}} \\ {b_{1{\_ h}} = {\frac{1}{m}{\sum\limits_{i = 1}^{m}\left( {P_{i,h} \cdot T_{i,h}} \right)}}} \end{matrix},{m \geq 2}} & (1) \end{matrix}$

-   -   2. Each time a successive measurement pair becomes available for         day d, A₀₁ _(—) _(h), A₁₁ _(—) _(h), b₀ _(—) _(h), b₁ _(—) _(h)         should be recursively updated as in equation 2.

$\begin{matrix} {{\forall h},\begin{matrix} {A_{01{\_ h}} = \frac{{\left( {N - 1} \right) \cdot A_{01{\_ h}}^{*}} + T_{d,h}}{N}} \\ {A_{11{\_ h}} = \frac{{\left( {N - 1} \right) \cdot A_{11{\_ h}}^{*}} + T_{d,h}^{2}}{N}} \\ {b_{0{\_ h}} = \frac{{\left( {N - 1} \right) \cdot b_{0{\_ h}}^{*}} + P_{d,h}}{N}} \\ {b_{1{\_ h}} = \frac{{\left( {N - 1} \right) \cdot b_{1{\_ h}}^{*}} + {P_{d,h} \cdot T_{d,h}}}{N}} \end{matrix}} & (2) \end{matrix}$

-   -   -   A*₀₁, A*₁₁, b*₀, and b*₁ are A₀₁, A₁₁, b₀, and b₁ from the             preceding iteration, respectively.

    -   3. Calculate the coefficients for the linear model using the         equation 3.

$\begin{matrix} {{\forall h},\begin{matrix} {a_{0{\_ h}} = \frac{{A_{11{\_ h}}b_{0{\_ h}}} - {A_{01{\_ h}}b_{1{\_ h}}}}{A_{11{\_ h}} - A_{01{\_ h}}^{2}}} \\ {a_{1{\_ h}} = \frac{b_{1{\_ h}} - {A_{01{\_ h}}b_{0{\_ h}}}}{A_{11{\_ h}} - A_{01{\_ h}}^{2}}} \end{matrix}} & (3) \end{matrix}$

-   -   4. Generate {circumflex over (P)} for the upcoming four days         using the linear model in equation 4:

for D={d+1,d+2,d+3,d+4}, and ∀h,{circumflex over (P)} _(D,h) =a ₀ _(—) _(h) +a ₁ _(—) _(h) ·T _(f) _(—) _(D,h)  (4)

-   -   If the d measurement pairs are stored and accessible, the hourly         standard deviation σ_(h), which is potentially a useful         indicator of the accuracy of and one's confidence in the hourly         prediction {circumflex over (P)}_(D,h), may be computed as         follows:

$\begin{matrix} {{\forall h},\begin{matrix} {\sigma_{h} = \sqrt{\frac{1}{d}{\sum\limits_{i = 1}^{d}\left( {P_{i,h} - {\hat{P}}_{i,h}} \right)^{2}}}} \\ {= \sqrt{\frac{1}{d}{\sum\limits_{i = 1}^{d}\left( {P_{i,j} - \left( {a_{0{\_ h}} + {a_{1{\_ h}} \cdot T_{i,h}}} \right)} \right)^{2}}}} \end{matrix}} & (5) \end{matrix}$

-   -   5. Generate L_(inelastic) _(—) _(n) by allocating {circumflex         over (P)}_(D,h) to each n^(th) interval:

$\begin{matrix} {{\forall n},{L_{inelastic\_ n} = \left\{ \begin{matrix} {\hat{P}}_{D,h} & {{{if}\mspace{14mu} n} \subseteq h} \\ \overset{\_}{{\hat{P}}_{D,h}} & {{{if}\mspace{14mu} h} \subseteq n} \end{matrix} \right.}} & (6) \end{matrix}$

-   -   -   {circumflex over (P)}_(D,h) is the average of all             {circumflex over (P)}_(D,h) corresponding to all hours h             lying within n.

    -   Make this L_(inelastic) _(—) _(n) prediction available as an         output of this function into the transactive node's algorithmic         toolkit framework.

    -   6. Repeat starting from step 2 above.

Subappendix A Additional Details about the Formulation

This formulation is based on a first-order polynomial (linear) model of power {circumflex over (P)} as a function of temperature T, as shown in equation A1. This model's coefficients a₀, and a₁ are determined via a least-squares error fit to pairs of measured power and temperature. The coefficients may be used thereafter to predict power given forecasted temperatures.

{circumflex over (P)}=a ₀ +a ₁ ·T  (A1)

The optimal coefficients are determined by minimization of the cost function J shown in equation A2. This wisely chosen cost function happens to be the statistical variance of the difference between actual measured electrical load and load that is modeled by the linear model during N days of a given type (weekdays, or weekends/holidays). The standard deviation is the square root of the variance. The variance and standard deviation are potentially useful indicators of the accuracy of and one's confidence in the predictions that result from this function.

$\begin{matrix} {J = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\; \left( {P_{i} - {\hat{P}}_{i}} \right)^{2}}}} & ({A2}) \end{matrix}$

The optimal coefficients are found by setting the partial derivatives of the cost function with respect to the two coefficients to zero, as shown in equation A3.

$\begin{matrix} {\begin{bmatrix} \frac{\partial J}{\partial a_{0}} \\ \frac{\partial J}{\partial a_{1}} \end{bmatrix} = {\begin{bmatrix} {{- \frac{2}{N}}{\sum\limits_{i = 1}^{N}\; \left( {P_{i} - a_{0} - {a_{1} \cdot T_{i}}} \right)}} \\ {{- \frac{2}{N}}{\sum\limits_{i = 1}^{N}\; \left( {{P_{i} \cdot T_{i}} - {a_{0} \cdot T_{i}} - {a_{1} \cdot T_{i}^{2}}} \right)}} \end{bmatrix} = 0}} & ({A3}) \end{matrix}$

Equation A3 can be written in matrix form, as in equation A4.

$\begin{matrix} {{\begin{bmatrix} 1 & {\frac{1}{N}{\sum\limits_{i = 1}^{N}\; T_{i}}} \\ {\frac{1}{N}{\sum\limits_{i = 1}^{N}\; T_{i}}} & {\frac{1}{N}{\sum\limits_{i = 1}^{N}\; T_{i}^{2}}} \end{bmatrix}\begin{bmatrix} a_{0} \\ a_{1} \end{bmatrix}} = \begin{bmatrix} {\frac{1}{N}{\sum\limits_{i = 1}^{N}\; P_{i}}} \\ {\frac{1}{N}{\sum\limits_{i = 1}^{N}\; \left( {P_{i} \cdot T_{i}} \right)}} \end{bmatrix}} & ({A4}) \end{matrix}$

The matrix is seen to be identical to its transpose. The simplified representation given in equation A5 will prove useful in referring to the various vector and matrix elements of equation A4.

$\begin{matrix} {{\begin{bmatrix} 1 & A_{01} \\ A_{01} & A_{11} \end{bmatrix}\begin{bmatrix} a_{0} \\ a_{1} \end{bmatrix}} = \begin{bmatrix} b_{0} \\ b_{1} \end{bmatrix}} & ({A5}) \end{matrix}$

This is in the form Ax=b, the solution of which can be found by x=A⁻¹b, as long as matrix A is invertible or nonsingular. Formulas exist for the inversion of a 2×2 matrix, so each coefficient may be explicitly solved for as in equation A6. This explicit representation is advantageous because it alleviates any expectation that the computational infrastructure being relied upon to conduct this function necessarily possesses any matrix solvers.

$\begin{matrix} {{a_{0} = \frac{{A_{11}b_{0}} - {A_{01}b_{1}}}{A_{11} - A_{01}^{2}}}{a_{1} = \frac{b_{1} - {A_{01}b_{0}}}{A_{11} - A_{01}^{2}}}} & ({A6}) \end{matrix}$

This method should not require a large set of training data, but some startup issues may be encountered. There is no reasonable way to predict electrical load before any comparable measurement has been made. If used non-recursively according to the formulation so far, the coefficients cannot be uniquely determined until at least two non-identical measurement pairs have been taken. Exceptions would be used to apply the method until N>2.

After two non-identical measurements, the problem becomes over-determined, and the power of least-squares error fit comes into play. The question then becomes how many samples N to maintain and use. If a moving window is used, then one should store a cache of N data pairs. Furthermore, the cache should be maintained for all of the more than 24×2 sets of hours and day types that are to be modeled. The moving window approach may not be especially efficient from a computational and storage standpoint and should be avoided. A recursive approach is preferred.

In a recursive formulation, one can keep a cache of only the four most recently calculated unique vector and matrix elements (A₀₁, A₁₁, b₀, and b₁) for each day type and its hours. Each of these elements is presumed to have already been influenced by at least N prior measurements. When a new measurement pair (P_(N+1), T_(N+1)) becomes available for this hour and hour type, one may recursively update elements as exemplified in A7 for vector element b₁. The effect of this recursive formula is that the old vector element is replaced by a new term that is a weighted sum of the old element and a new term that uses the new measurements. If N is large, the new measurements have less impact than they would if N were small.

$\begin{matrix} {b_{1} = \frac{{{\left( {N - 1} \right) \cdot \frac{1}{N}}{\sum\limits_{i = 1}^{N}\; \left( {P_{i} \cdot T_{i}} \right)}} + {P_{N + 1} \cdot T_{N + 1}}}{N}} & ({A7}) \end{matrix}$

Equation A8 more simply and generally shows how the old vector element b*₁ becomes replaced by the new one b₁. The two weighting factors are (N−1)/N and 1/N, which sums to unity.

$\begin{matrix} {b_{1} = \frac{{\left( {N - 1} \right) \cdot b_{1}^{*}} + {P_{N + 1} \cdot T_{N + 1}}}{N}} & ({A8}) \end{matrix}$

Nothing prevents the application of recursive formulas of the type exemplified by A7 and A8 after the elements have been initialized. The first predictions may be wild and unreliable until more measurements can become incorporated into the model.

Subappendix B Example

In this example, real power (load) P and temperature T measurements during fourteen weekdays—given in Table 34 and Table 35, respectively—are used to compute {circumflex over (P)}, following the procedure outlined in the Pseudo Code Implementation section. The resulting {circumflex over (P)} is given in Table 36, and plotted along with ±1 standard deviation (e.g. ±√{square root over (J)}) and P in the set 5100 of graphs in FIG. 51. Notice that the “NaN” (not a number) entries on day 3 are due to the singularity of matrix A caused by the identical temperature points at the corresponding hours on days 1 and 2. FIG. 52 through FIG. 54 are sets 5200, 5300, 5400 of graphs that show the linear least-squares error fit for each hour of the day, for days 4, 12, and 14, respectively, given the measured data.

TABLE 34 Power P Measurements in kW D 1 2 3 4 5 6 7 8 9 10 11 12 13 14 h 0 126630 126380 123750 119310 108010 91850 101540 99580 110370 118090 111810 108690 94420 99760 1 128540 127530 126080 119370 106720 90490 101250 99270 110440 115540 112920 107110 92590 99970 2 130030 132390 128840 118230 107120 90680 102500 99460 112350 115970 114350 106530 92940 101600 3 132300 134530 129970 119680 109430 92310 104400 100900 116400 117040 118210 108160 93430 103750 4 136720 137780 131020 120650 110730 93910 105960 102830 120110 117440 121380 108240 95000 106250 5 141660 143280 135970 122840 113740 96180 110190 107220 126350 120040 127690 112090 99450 111410 6 151840 151040 144810 131840 121820 105230 119760 117030 135810 127720 135390 120230 108640 120590 7 164120 161680 157710 142160 132860 114240 130250 129380 146520 138180 145470 129690 116230 131540 8 166680 162390 158210 142940 134880 116610 131660 126770 151070 140760 150230 130020 115310 131170 9 158610 156650 150760 137720 132790 116310 126940 121170 146550 138550 145140 127470 112020 121080 10 150280 145960 144010 131050 130430 107040 119110 113870 137590 135270 135700 123850 107310 114660 11 140770 138850 138650 120960 124670 100140 114120 107110 128370 128050 128430 120340 104290 108770 12 132130 130430 134000 110740 120430 96160 111270 101900 120040 116560 122470 115930 103010 105390 13 125840 125450 131130 105590 115060 96720 103900 97780 113440 109900 115470 114020 103600 103400 14 120530 119940 130460 102400 114400 93370 102900 94950 110830 106170 114590 114710 105380 101570 15 118960 117000 129940 102900 111120 94600 101420 92960 109080 102160 117490 116450 106720 102620 16 116740 116360 131310 103930 111810 94570 102470 94420 109880 102600 118930 117110 110980 103650 17 123890 121190 135200 107620 117810 102710 108120 99210 115810 108130 125210 119550 114430 111170 18 137920 135820 141200 118540 125000 110720 119310 111320 128410 120590 131300 126230 121330 118390 19 142510 139340 141880 122890 123340 114190 121300 118170 132660 124750 133520 126760 121940 123540 20 142980 138900 139110 122620 119860 114130 118760 119720 134420 126250 128930 122130 116690 122810 21 142550 137650 135470 122060 117290 111990 115170 117520 131880 124860 125790 116520 111650 120670 22 136270 133130 130030 117390 112710 107870 109270 114110 128100 121910 120550 107950 110480 114810 23 129740 126930 121660 112760 106290 103520 102800 111150 121650 117880 111880 99490 100620 107230

TABLE 35 Temperature T Measurements in ° C. d 1 2 3 4 5 6 7 8 9 10 11 12 13 14 h 0 −21.00 −22.00 −21.00 −14.00 −14.00 −4.00 −12.00 −10.00 −16.00 −17.00 −22.00 −7.00 3.00 −3.00 1 −22.00 −22.00 −21.00 −14.00 −13.00 −4.00 −11.00 −9.00 −17.00 −14.00 −21.00 −7.00 3.00 −4.00 2 −22.00 −23.00 −21.00 −13.00 −13.00 −4.00 −12.00 −9.00 −17.00 −14.00 −21.00 −6.00 2.00 −6.00 3 −23.00 −23.00 −19.00 −12.00 −12.00 −4.00 −13.00 −8.00 −23.00 −12.00 −21.00 −7.00 3.00 −6.00 4 −22.00 −22.00 −20.00 −12.00 −10.00 −5.00 −11.00 −8.00 −20.00 −11.00 −21.00 −6.00 2.00 −6.00 5 −23.00 −24.00 −20.00 −12.00 −10.00 −5.00 −10.00 −8.00 −22.00 −11.00 −19.00 −6.00 3.00 −7.00 6 −23.00 −24.00 −20.00 −12.00 −11.00 −8.00 −9.00 −8.00 −23.00 −11.00 −19.00 −5.00 3.00 −7.00 7 −24.00 −23.00 −20.00 −11.00 −10.00 −7.00 −8.00 −9.00 −22.00 −11.00 −19.00 −4.00 3.00 −7.00 8 −23.00 −22.00 −18.00 −10.00 −9.00 −9.00 −8.00 −13.00 −22.00 −11.00 −21.00 −4.00 3.00 −7.00 9 −19.00 −19.00 −16.00 −9.00 −9.00 −8.00 −8.00 −12.00 −20.00 −10.00 −18.00 −3.00 3.00 −6.00 10 −17.00 −16.00 −14.00 −8.00 −8.00 −7.00 −7.00 −9.00 −16.00 −8.00 −16.00 −2.00 4.00 −5.00 11 −16.00 −14.00 −13.00 −4.00 −8.00 −5.00 −2.00 −9.00 −15.00 −8.00 −13.00 −2.00 1.00 −5.00 12 −13.00 −13.00 −11.00 −4.00 −7.00 −3.00 −2.00 −6.00 −13.00 −8.00 −9.00 −2.00 3.00 −5.00 13 −12.00 −11.00 −10.00 −4.00 −6.00 −2.00 −1.00 −6.00 −12.00 −5.00 −7.00 −2.00 2.00 −4.00 14 −11.00 −8.00 −9.00 −4.00 −6.00 −1.00 −2.00 −4.00 −11.00 −4.00 −5.00 −1.00 2.00 −4.00 15 −11.00 −7.00 −9.00 −4.00 −5.00 −1.00 −2.00 −4.00 −10.00 −4.00 −6.00 −1.00 3.00 −4.00 16 −12.00 −7.00 −9.00 −5.00 −4.00 1.00 −3.00 −5.00 −7.00 −5.00 −6.00 −1.00 3.00 −3.00 17 −11.00 −8.00 −9.00 −3.00 −3.00 −1.00 −3.00 −6.00 −9.00 −4.00 −6.00 0.00 3.00 −4.00 18 −16.00 −9.00 −9.00 −8.00 −4.00 −2.00 −4.00 −7.00 −11.00 −13.00 −6.00 −1.00 3.00 −5.00 19 −18.00 −12.00 −11.00 −8.00 −4.00 −2.00 −7.00 −13.00 −16.00 −11.00 −6.00 0.00 3.00 −6.00 20 −19.00 −19.00 −12.00 −11.00 −5.00 −5.00 −6.00 −12.00 −16.00 −19.00 −6.00 0.00 3.00 −6.00 21 −19.00 −19.00 −12.00 −12.00 −5.00 −5.00 −6.00 −10.00 −16.00 −20.00 −7.00 1.00 0.00 −7.00 22 −21.00 −19.00 −15.00 −13.00 −4.00 −11.00 −10.00 −12.00 −18.00 −18.00 −7.00 2.00 −3.00 −7.00 23 −18.00 −19.00 −14.00 −15.00 −4.00 −11.00 −11.00 −17.00 −17.00 −20.00 −6.00 2.00 −3.00 −7.00

TABLE 36 Predicted Load {circumflex over (P)} in kW d 1 2 3 4 5 6 7 8 9 10 11 12 13 14 h 0 — — 126630 124191 119498 96626 108269 102805 114650 116329 127101 96474 85579 98352 1 — — NaN 112395 118196 94929 105910 100573 117443 110287 125689 96505 83045 98352 2 — — 127670 112362 117984 94487 108956 100904 118733 111250 127527 94263 84303 101254 3 — — NaN 123941 120116 101062 113747 101384 133700 109346 127434 101173 86802 103607 4 — — NaN 106100 116809 103093 111749 103192 132447 109778 133525 100222 87433 104971 5 — — 136800 121561 119302 106235 112052 106958 139419 113423 131534 103789 88863 110968 6 — — 154240 134747 130419 119543 115100 114163 150572 121766 139918 109873 98412 119912 7 — — 154360 142393 140515 130391 125117 129120 159899 133373 151689 118670 109277 130146 8 — — 145230 143146 141225 137822 127300 141211 163014 133587 158918 118595 109151 130867 9 — — NaN 134730 137507 132860 126121 137835 160160 129541 152236 113491 107649 127317 10 — — 137320 127838 130750 128511 119661 125622 146717 120059 145536 107769 103493 122319 11 — — 137890 130499 128391 121910 105218 125576 138497 120412 132764 107940 107169 117351 12 — — NaN 143520 118649 110789 100537 114747 131530 117284 119690 102612 97249 114317 13 — — 125060 137416 112662 104182 97282 112642 126957 107679 112801 101343 97428 110096 14 — — 120137 121422 113458 103761 100347 106438 124786 104472 107208 98929 98885 109909 15 — — 117980 116726 111867 105021 102011 106541 120376 104146 108635 97775 97505 109736 16 — — 116512 118213 112656 108185 106796 109286 111196 107311 108670 100318 100467 109828 17 — — 122090 119859 111253 111212 111197 116548 121170 110107 114007 102980 105753 115742 18 — — 135820 136638 129992 126212 123068 126902 131885 134883 122509 117508 116577 125388 19 — — 138812 138298 127833 122911 127317 136713 139585 130864 122535 117216 118237 127802 20 — — NaN 138849 120367 120023 119184 129288 135266 140450 118000 113418 114014 124063 21 — — NaN 135470 116724 117127 116668 123607 134221 141567 117392 107961 113130 121303 22 — — 126850 126981 103428 121141 116431 118619 129812 129610 107833 98446 109999 115050 23 — — 140980 124582 95629 115900 113221 123598 122204 128027 101939 95493 103983 108230

6.3.3 Transactive Imported Energy Function 1.2 Description

Converts transactive signals from transactive neighbors into framework parameter outputs that are expected by the toolkit framework.

Application: A transactive node typically should restate the transactive signals that it receives in terms of toolkit framework parameters.

This toolkit function is so basic that it may be treated as part of the toolkit framework.

Block Input/Output Function Model:

Inputs: Current IST time series. Transactive incentive signals (TIS) from each transactive neighbor. Transactive feedback signals (TFS) from each transactive neighbor. Outputs: TIS restated as energy terms C_(E). TFS restated as energy terms P_(G) for the intervals during which the TFS represents imported energy.

6.3.4 Small Wind Generator Negative Load Function 1.4 Description

This function is to predict the power to be produced by small wind energy resources. This function is preferred where a relatively small amount of wind renewable generation offsets load at a location.

If the energy from a wind energy resource should directly affect the transactive incentive signal (TIS) at this location and electrically downstream locations, the energy from this resource should be incorporated with the Wind Energy resource and incentive toolkit function instead.

This function applies to locations that host relatively small wind generators or wind sites that primarily offset a larger electrical load.

Block Input/Output Function Model: Inputs:

-   -   {u_(k)}—[m/s]—Time series of predicted wind speed for a future         interval k, for the upcoming four days (time horizon of         transactive signals), based on wind speed data recorded at a         height h, at or close to the location under consideration.         Although granular data is desired, this function is formulated         to work with any available data interval.     -   h—[m]—Height at which wind speed is predicted.     -   ψ—[unitless]—Wind turbine manufacturer and model information to         be chosen from this preliminary text enumeration:         -   Honeywell WT6500         -   Windspire 1.2         -   Home Energy Americas V200         -   Skystream         -   Bergey Excel 10         -   Urban Green Energy UGE-4K         -   Tangarie Gale 10         -   WePower Falcon 5.5         -   Wing-Power Prototype     -   This enumeration should be augmented whenever a new wind turbine         is to be considered.     -   m—[count]—Number of wind turbines.     -   h_(hub)—[m]—wind turbines' representative hub height.     -   K_(n)—[unitless]—Time series availability fraction, e.g.         fraction of turbines or wind site that is predicted to be online         during each n^(th) IST interval, where n=0, 1, . . . , 55. Wind         generation may be limited or entirely unavailable due to         maintenance schedules and other reasons.

Interim Calculation Products:

-   -   {u_(hub,k)}—[m/s]—Time series of predicted wind speed for a         future interval k at the wind turbines' representative hub         height h_(hub).

Output:

-   -   {L_(n)}—[kW]—Time series of average power to be produced by wind         turbine(s) for each future n^(th) IST interval.

Pseudo Code Implementation:

-   -   1. Restate inputs in the units specified in previous section, if         necessary.     -   2. Compute {u_(hub,k)}:         -   Based on the wind profile power law relationship (Elliot             1986):

$\begin{matrix} {{\forall k},{u_{{hub},k} = {\left( \frac{h_{hub}}{h} \right)^{\alpha} \cdot u_{k}}}} & (1) \end{matrix}$

-   -   -   -   α—[unitless]—An empirically derived constant for the                 location of the wind turbine(s). If empirical derivation                 is not possible, 1/7 may be used as an approximation.

        -   The implementer may choose to use a different             approach/relationship, if deemed more appropriate/accurate.

    -   3. Generate {L_(n)}:         -   For the given ψ input, generate {L_(k)} by looking up, from             Table 37, an L_(k) corresponding to each u_(hub,k):

TABLE 37 Lookup table for wind turbine power output at a given wind speed L [kW] ome Energy Bergey Urban Green WePower Wing- u Honeywell Windspire Americas Skystream Excel Energy UGE- Tangarie Falcon Power [m/s] WT6500 1.2 V200 3.7 10 4K Gale 10 5.5 Prototype 1.0 0.009 0 0 0 0.020 0 0 0 0 1.5 0.015 0 0 0 0.030 0 0 0 0 2.0 0.025 0 0 0 0.080 0 0.333 0 0 2.5 0.038 0 0 0 0.105 0.041 0.617 0 0 3.0 0.048 0 0.005 0 0.159 0.082 0.833 0.066 0.029 3.5 0.074 0 0.014 0.024 0.254 0.123 1.167 0.166 0.072 4.0 0.103 0.030 0.025 0.072 0.382 0.185 1.417 0.298 0.130 4.5 0.128 0.065 0.040 0.144 0.636 0.247 1.667 0.464 0.202 5.0 0.171 0.115 0.059 0.220 0.891 0.309 2.167 0.633 0.276 5.5 0.209 0.160 0.082 0.336 1.209 0.391 2.667 0.895 0.391 6.0 0.251 0.220 0.100 0.456 1.527 0.514 3.083 1.127 0.492 6.5 0.285 0.283 0.122 0.600 2.036 0.658 3.583 1.358 0.593 7.0 0.333 0.350 0.145 0.744 2.482 0.823 4.167 1.590 0.694 7.5 0.392 0.425 0.178 0.936 2.991 0.988 4.833 1.855 0.809 8.0 0.457 0.525 0.225 1.104 3.627 1.193 5.500 2.087 0.911 8.5 0.500 0.610 0.285 1.320 4.391 1.440 6.167 2.319 1.012 9.0 0.583 0.750 0.372 1.542 5.218 1.708 6.833 2.584 1.128 9.5 0.651 0.880 0.460 1.780 6.109 2.058 7.667 2.916 1.272 10.0 0.714 1.025 0.552 2.000 6.936 2.366 8.417 3.346 1.460 10.5 0.793 1.138 0.642 2.136 7.891 2.675 9.250 3.877 1.692 11.0 0.888 1.188 0.733 2.254 8.909 3.086 10.000 4.340 1.894 11.5 0.981 1.200 0.822 2.325 10.055 3.601 11.000 4.771 2.082 12.0 1.069 1.200 0.900 2.372 10.945 4.012 12.000 5.102 2.226 12.5 1.172 1.175 1.005 2.396 11.709 4.074 13.083 5.300 2.313 13.0 1.250 1.138 1.100 2.410 12.091 4.000 14.167 5.400 2.356 13.5 1.357 1.000 1.214 2.410 12.345 4.000 15.167 5.500 2.400 14.0 1.466 0.300 1.325 2.396 12.473 4.000 16.417 5.500 2.400

The information in Table 37 is plotted in graph 5500 of FIG. 55. Table 37 is based on information available in the datasheets or brochures of these wind turbines. The powers given for 32 m/s are to be used for speeds beyond 32 m/s. The datasheet of this wind turbine claims that it does not have a cut-out wind speed. Therefore, the power output has been extrapolated beyond 20 m/s. However, the extrapolated data should be replaced if more accurate data is available. This wind turbine has a cut-out speed of 30 m/s, but power output data between 20 and 30 m/s is missing in its datasheet. This data has been extrapolated here, but should be replaced if more accurate data is available. No cut-out speed information is given in the datasheet of this wind turbine. It is assumed that there is no cut-out speed and the power output has been extrapolated beyond 14 m/s. This data has been extrapolated here, but should be replaced if more accurate data is available. There is no datasheet for this prototype wind turbine. Given its similarities with the WePower Falcon 5.5, its power versus wind speed data is assumed to be a scaled version of the Falcon 5.5. However, this data should be replaced by either empirical data or such data from a different source.

-   -   Allocate {L_(k)} to each n^(th) interval, scale by m, and         multiply by K to generate {L_(n)}:

$\begin{matrix} {{\forall n},{L_{n} = \left\{ \begin{matrix} {{m \cdot K_{n} \cdot L_{k}},} & {{{if}\mspace{14mu} n}\; \subseteq k} \\ {{m \cdot K_{n} \cdot \overset{\_}{L_{k}}},} & {{{if}\mspace{14mu} k} \subseteq n} \end{matrix} \right.}} & (2) \end{matrix}$

-   -   -   L_(k) —[kW]—weighted-average of all L_(k) within n.

    -   Make this {L_(n)} prediction available as an output of this         function into the transactive node's algorithmic toolkit         framework.

6.3.5 Small-Scale Solar Generator Negative Load Function 1.6 Description

This function is to predict the power to be produced by small solar energy resources. This function is preferred where a relatively small amount of solar renewable generation offsets load at a location.

If the energy from a solar energy resource should directly affect the transactive incentive signal (TIS) at this location and electrically downstream locations, the energy from this resource should be incorporated with the Solar Energy resource and incentive toolkit function instead.

This function applies to locations that host relatively small solar generators or solar sites that primarily offset a larger electrical load.

Block Input/Output Function Model: Inputs:

-   -   {GTI_(k)}—[kW/m²]—Time series of predicted Global Tilted         Irradiance (GTI) for a future interval k, for the upcoming four         days (time horizon of transactive signals), based on solar         irradiance data recorded at or close to the location under         consideration. (GTI=DNI·cos(θ_(i))+DIF·(1−β180°), where DNI is         the Direct Normal Irradiance, DIF the Diffuse Horizontal         Irradiance, β the inclination angle of the tilted plate, and         θ_(i) the angle between DNI and the normal of the tilted plate.         DNI and DIF are the actual data measured at the location under         consideration. Furthermore, cos(θ_(i))=cos β·cos Z+sin β·sin         Z·cos(θ−ψ), where θ and Z are the sun's azimuth and zenith,         respectively. Note that there are known equations to compute θ         and Z throughout the day, every day, at a given latitude.)         Although granular data is desired, this function is formulated         to work with any available data interval. The GTI represents the         effective irradiance normal to a tilted surface. For a fixed         flat-plate photovoltaic (PV) collector, the computation of GTI         is, therefore, dependent on its inclination angle β and azimuth         ψ, as defined below. Note also that GTI may not be shared         amongst solar generators unless they have the same inclination         and azimuth. For a solar-tracking collector or concentrating         collector, the computation of GTI should assume that the normal         of the solar collector is in line with the Direct Normal         Irradiance (DNI).     -   β—[°]—Inclination angle of the fixed flat-plate PV collector.         This is 0° for systems laying horizontal to the ground. This is         not required for solar-tracking collectors, including         concentrating collectors.     -   ψ—[°]—Azimuth of the fixed flat-plate PV collector. This is 180°         for systems facing due south. This is not required for         solar-tracking collectors, including concentrating collectors.     -   A—[m²]—Effective surface area of the solar collector.     -   η—[%]—Overall conversion efficiency of the solar energy         resource, e.g. from the incident solar power (e.g., GTI·A) to         the usable alternating current (AC) power. This should be the         product of the efficiencies of the solar collector and its power         converter, and, if possible, should include conduction losses.         The implementer may choose to model this overall efficiency as a         function of power. While the efficiency of the solar collector         may be constant at different power levels, the efficiency of the         power converter varies. The efficiency versus power curve of the         converter is sometimes published in its datasheet. Conduction         losses also vary with power, but may be harder to quantify and         model.     -   m—[count]—Number of such solar energy resources that is being         modeled by this function.     -   K_(n)—[unitless]—Time series availability fraction, e.g.         fraction of solar energy resources or solar site that is         predicted to be online during each n^(th) IST interval, where         n=0, 1, . . . , 55. Solar generation may be limited or entirely         unavailable due to maintenance schedules and other reasons.

Interim Calculation Product:

-   -   {L_(k)}—[kW]—Time series of average power to be produced by one         solar energy resource for each future interval k.

Output:

-   -   {L_(n)}—[kW]—Time series of average power to be produced by the         solar energy resource(s) for each future n^(th) interval.

Pseudo Code Implementation:

-   -   1. Restate inputs in the units specified in previous section, if         necessary.     -   2. Generate {L_(k)}:         -   For each future interval k, compute the average power             {L_(k)} to be produced by one solar energy resource:

∀k,L _(k)=GTI_(k) ·A·η  (2)

-   -   3. Generate {L_(n)}:         -   Allocate {L_(k)} to each n^(th) interval, scale by m, and             multiply by K_(n) to generate {L_(n)}:

$\begin{matrix} {{\forall n},{L_{n} = \left\{ \begin{matrix} {{m \cdot K_{n} \cdot L_{k}},} & {{{if}\mspace{14mu} n}\mspace{11mu} \subseteq k} \\ {{m \cdot K_{n} \cdot L_{k}},} & {{{if}\mspace{14mu} k} \subseteq n} \end{matrix} \right.}} & (3) \end{matrix}$

-   -   -   -   L_(k) —[kW]—weighted-average of all L_(k) within n.

    -   Make this {L_(n)} prediction available as an output of this         function into the transactive node's algorithmic toolkit         framework.

6.3.6 General Event-Driven Demand Function 2.0 Description

This is a very general function for predicting the behaviors of responsive load assets that only infrequently respond to events that may be identified from an incentive signal. When these assets respond, they transition to a limited number of available response levels. This general function may serve as a template for functions that are more narrowly targeted to specific responsive asset systems. This function has been written at such a high level that it will not likely be referenced and used for any asset system. But this function description will be valuable guidance to those who design more specific functions for more specific asset systems.

This function can respond to absolute or relative TIS as desired by an application.

This function applies to many responsive asset systems that conduct traditional demand response several times a month. Response may additionally define a “critical” response level for extreme conditions.

Block Input/Output Function Model: Inputs:

Current IST time series. TIS time series. Recent history (e.g., 1 day to 1 week) of TIS that may be used if relative TIS is to be tracked in a statistical sense. Numbers of assets in this asset system population that may be used to scale this function. Typical daily or weekly inelastic load profile for the asset systems that are being predicted by this function. This profile is a starting point for predicting the inelastic load component.

Outputs:

Predicted inelastic load at for each IST interval. Predicted change in elastic load for each IST interval. Predicted advisory control signal for this asset system.

Pseudo Code Implementation:

Inelastic load component. This algorithm will not predict an inelastic load component. Inelastic load components are better addressed by inelastic load functions that have been defined. Elastic load component. This algorithm will calculate (1) predicted change in electrical load in response to the incentive signal (e.g., the asset's elasticity), (2) “events” during which an asset is predicted to respond, and (3) the predicted advisory control signal that will be sent to this elastic asset system. Predicted Change in Electrical Load in Response to the Incentive Signal. To predict a change in energy that can result from this asset system during events, this function should model the consumption (or generation) of energy by this asset system. At least two approaches can be accommodated: (1) An explicit time-series load shape may be used to represent the responsive load (or generation) from this asset system. Alternatively, (2) A dynamic model of this asset system may be simulated to predict the effect that an event will have on the asset system. These approaches will be compared by discussing how each one could be used to predict the change in electrical load that could be had from a set of residential tank water heaters. Explicit Time-series Load Shape. The average electrical load consumed during each hour of a day by a residential 40-gallon tank electric water heater may be obtained. In some cases, regional and seasonal variations may be found. See (Hammerstrom 2007, FIG. 4.18) for example. The load curves represent the average power that is expected to be consumed by an electric water heater at any time of the day. In many cases, splines will allow such load curves to be very efficiently stored and reproduced. The number of water heaters in the asset system population is a scaling factor that may be used to predict the entire consumption by this population of water heaters. If an event were to occur and cause this population of water heaters to become curtailed, the change in energy consumption by these water heaters would be predicted well by knowing the number of water heaters, the representative load curve for a single water heater, and the time and duration of the event. Dynamic Asset System Model. The same population of electric water heaters may be more rigorously modeled using a physics-based model of a water heater. In this case, one could input typical residential hot water consumption instead of an electrical load curve. As water is consumed, hot water leaves the water tank, cold water enters the water tank, and the temperature of the water in the tank decreases. The modeled thermostat turns on the electrical heating element and heats the water at a rate that is determined by the power rating of a heating element. If the model being used is accurate, the resulting electrical load curve would also be accurate on a “typical” day.

However, if a curtailment is predicted, the response of the dynamic water heater model can predict secondary effects that could not have been modeled otherwise. After a period of electrical curtailment, the water in the tank will have become relatively cold. When the curtailment period ends, additional energy is then used to reheat the cool, stored water to the desired temperature. A rebound effect is thereby predicted at the conclusion of the curtailment event.

Events during which this Asset is Predicted to Respond. The capabilities and availability of the modeled asset system determine a set of incentive thresholds that should be managed by this function. A threshold may be a function of time. An asset system that has only two modes of operation (e.g., normal and curtailed) will define only one threshold. Generally, an asset system that has m modes of operation should define m−1 thresholds. The resulting thresholds, in turn, define m−1 levels of response for an asset system. (The “Normal” mode of operation is indeed a mode of operation, but it is usually not considered a response level.) “Events” occur any time that the predicted incentive signal exceeds a defined threshold to invoke one of the levels of response that is a feature of this asset system.

The availability of asset systems that are responsive either on an event-based or time-of-use basis may be predicted if limitations on the numbers and durations of events are stated. For example, a utility might have contracted with its customers that a responsive asset will not become curtailed by the utility more often than four times per calendar month and that none of these curtailments will not endure for more than 2 hours.

Over time, statistical distributions and correlations emerge from the dynamic behaviors of the incentive signal. This function may incorporate the behaviors of past historical incentive signals and the predicted incentive signals as these statistics are being compiled. This function may thereafter refer to such statistics to evaluate and predict where a threshold should be placed to initiate just fewer than the allowed number of events and just less than the allowed duration of events. Automated event-driven demand response will be attempting to identify events within monthlong durations, so these functions should use the actual incentive signal (not its statistical average), or it should track the statistical average of the incentive signal quite slowly in comparison with that duration.

Predicted Advisory Control Signal. Once events have been predicted, the predicted advisory control signal may be stated, aligned in time with the predicted events, according to the standardized method described in the appendix entitled “Standard Advisory Output Control Signal”. In the referenced method, the capabilities of this asset system and, in some cases, the severity of an event determine which integer member of a signed byte signal will be sent to the asset system. (The domain of relevant advisory control signals will be relatively small for functions that are formulated for specific asset systems.)

6.3.7 Incentive Function Wind Energy (Function 2.1) Description

This function addresses wind power generation and is to be applied at transactive nodes which have and represent wind farm energy that is produced within or near their electrical boundaries to encourage the use of wind energy when and near where it is generated. This function is applicable to energy produced by a wind farm or may be applied to aggregated output from multiple wind farms.

The cost of supplying the wind energy generated is applied as an infrastructure cost, in units of cost per time, consistent with the Transactive Node Framework. For simplicity, the infrastructure cost will use the $2155/kW capacity-weighted average installed cost for a wind farm. The infrastructure cost of a wind farm can thus be estimated if its capacity is known. This cost shall then be spread over the lifetime T of the wind farm.

Note that this calculation typically yields an infrastructure cost near $0.010/kW/h ($10/MW/h) if a 25-year lifetime is assumed. It is permissible for the implementer of this function to assume that T=2.19×10⁵ hours (25 years) if better estimates are unavailable for the lifetime of the wind farm installation.

After a wind farm exceeds its planned lifetime, a decision should be made. Thereafter, the infrastructure cost may be (a) zeroed out, (b) replaced by ongoing maintenance costs, or (c) continued as before as an ongoing replacement cost. This function should be revisited and refined when this situation will be encountered.

This function should also predict the electrical power that will be produced by the wind resource during each future interval. An explicit algorithm could be created to convert predicted weather conditions (like wind speed and direction) into electrical power output. This function will assume that experts satisfy this goal by predicting electrical power output from meteorological data that is available to them.

Block Input/Output Function Model: Inputs:

P—Wind farm capacity/power rating. T—Lifetime of wind farm. IST_(n)—Present time series interval start times used by an example toolkit framework, where n=0, 1, . . . , 56. (There is no prediction to correspond with IST_(n) for n=56. This last IST is simply used to make it clear when the final interval concludes.) Meteorological data—Predicted wind speed, wind direction, relative humidity and perhaps other weather data that experts may use to predict electrical power production for wind farms.

Outputs:

C_(I,n)—Time series of infrastructure cost terms expected by the Transactive Node Framework (unit: $/h); series members correspond to IST_(n). Infrastructure costs are not expected to be dynamic, but it is specified as a time series for consistency with the Transactive Node Framework. P_(G,n)—Time series of predicted electrical power generated by wind farm (unit: average kW); series members correspond to IST_(n). C_(E,n)—Time series of energy cost terms (unit: cost per energy). Since the cost of supplying the wind energy generated is applied purely as an infrastructure cost, these energy cost terms should simply be set to zero. Note that these terms go in pair with the P_(G,n) terms and are used by the Transactive Node Framework.

Pseudo Code Implementation:

-   -   1. If necessary, restate P in kW and T in h (hour).     -   2. Compute the infrastructure cost C_(I,n) corresponding to         IST_(n) for n, as in equation (1).

$\begin{matrix} {{C_{I,n} = \frac{\left( {{\$ 2155}\text{/}{kW}} \right) \times P}{T}},{{{for}\mspace{14mu} n} = 0},1,\ldots \mspace{14mu},55} & (1) \end{matrix}$

-   -   3. Predict the average wind electrical power output P_(G,n) that         will be generated during each future interval corresponding to         IST_(n) for n.     -   4. Output C_(E,n)=0, for n=0, 1, . . . , 55.

6.3.8 Incentive Function Solar Energy (Function 2.2) Description

This function addresses solar power generation and is to be applied at transactive nodes which have and represent solar farm energy that is produced within or near their electrical boundaries to encourage the use of solar energy when and near where it is generated. This function is applicable to energy produced by a solar farm or may be applied to aggregated output from multiple solar farms.

The cost of supplying the solar energy generated is applied as an infrastructure cost, in units of cost per time, consistent with the Transactive Node Framework. For simplicity, the infrastructure cost will use the $7.5/W capacity-weighted average installed cost for a solar farm. The infrastructure cost of a solar farm can thus be estimated if its capacity is known. This cost shall then be spread over the lifetime T of the solar farm.

Note that this calculation typically yields an infrastructure cost near $0.034/kW/h ($34/MW/h) if a 25-year lifetime is assumed. It is permissible for the implementer of this function to assume that T=2.19×10⁵ hours (25 years) if better estimates are unavailable for the lifetime of the solar farm installation.

After a solar farm exceeds its planned lifetime, a decision should be made. Thereafter, the infrastructure cost may be (a) zeroed out, (b) replaced by ongoing maintenance costs, or (c) continued as before as an ongoing replacement cost. This function should be revisited and refined when this situation will be encountered.

This function should also predict the electrical power that will be produced by the solar resource during each future interval. An explicit algorithm could be created to convert predicted weather conditions (like solar irradiance and temperature) into electrical power output. This function will assume that experts satisfy this goal by predicting electrical power output from meteorological data that is available to them.

Block Input/Output Function Model: Inputs:

P—Solar farm capacity/power rating. T—Lifetime of solar farm. IST_(n)—Present time series interval start times used by the toolkit framework, where n=0, 1, . . . , 56. (There is no prediction to correspond with IST_(n) for n=56. This last IST is simply used to make it clear when the final interval concludes.) Meteorological data—Solar irradiance, temperature, and perhaps other weather data that experts may use to predict electrical power production for solar farms.

Outputs:

C_(I,n)—Time series of infrastructure cost terms expected by the Transactive Node Framework (unit: $/h); series members correspond to IST_(n). Infrastructure costs are not expected to be dynamic, but it is specified as a time series for consistency with the Transactive Node Framework. P_(G,n)—Time series of predicted electrical power generated by solar site (unit: average kW); series members correspond to IST_(n).

C_(E,n)—Time series of energy cost terms (unit: cost per energy). Since the cost of supplying the solar energy generated is applied purely as an infrastructure cost, these energy cost terms should simply be set to zero. Note that these terms go in pair with the P_(G,n), terms and are used by the Transactive Node Framework.

Pseudo Code Implementation:

-   -   1. If necessary, restate P in W and T in h (hour).     -   2. Compute the infrastructure cost C_(I,n) (units: $/h)         corresponding to IST_(n) for n, as in equation (1).

$\begin{matrix} {{C_{I,n} = \frac{\left( {{\$ 7}{.5}\text{/}W} \right) \times P}{T}},{{{for}\mspace{14mu} n} = 0},1,\ldots \mspace{14mu},55} & (1) \end{matrix}$

-   -   3. Predict the average solar electrical power output P_(G,n)         that will be generated during each future interval corresponding         to IST_(n) for n.     -   4. Output C_(E,n)=0, for n=0,1, . . . , 55.

6.3.9 Incentive Function Hydropower (Function 2.3) Description

This function is to predict the amount and cost of hydroelectric energy when and near where it is generated. It should at least represent federal hydropower of the region, but should strive to represent all regional hydropower. This function applies to transactive nodes that own or represent hydropower generation within their electrical boundaries. At least transmission zones 4, 5, 6, 7, 8, 10, 11, 12, and 14 are within the Columbia River Basin and would be expected to host federal hydropower. Based on the predicted generated powers of non-hydro sources at a transactive node and their associated costs of energy, and historical electricity market prices, this function predicts the weighted-average cost of energy of hydropower generation.

Block Input/Output Function Model: Inputs:

-   -   {P_(s,t)}, t=t₀, t₀+1 hour, . . . , t₀+i, . . . , t₀+l, . . .         —[kW]—Aggregated hourly scheduled hydropower generation (both         must-run and flexible), at the transactive node at which this         function is being implemented, for each hour of at least the         next four days (e.g., the predicted time horizon of the         transactive signals). Where this input cannot be known, trends         may be used.     -   {C_(m,h,d)}, h=00:00, 01:00, . . . , 23:00; d=−1, −2, . . . ,         −7—[$/kWh]—Historical electricity market price trading for every         hour starting at h of the day d, for the past 7 days. (A trend         based on the electricity market price for the past 7 days is         more likely to represent the expected market price for the next         four days.) The Dow Jones Mid-Columbia Electricity Price Indexes         is an example of a source for such information.     -   {K_(h,s)}, h=00:00, 01:00, . . . , 23:00, s=four seasons of the         year—[unitless]—fraction/percentage of total scheduled         hydropower generation, representing an estimate for flexible         hydropower generation during every hour starting at h of a given         season s.     -   C_(mustrun)—[$/kWh]—cost of energy for must-run hydropower         generation. This cost may have to be updated yearly, seasonally,         or at some shorter interval. (This cost of energy for must-run         hydropower generation is an estimate obtained from a         hypothetical supply stack provided by BPA and included in         Subappendix A.)

Interim Calculation Products:

-   -   { C_(trend,h)}, h=00:00, 01:00, . . . , 23:00—[$/kWh]—Trended         electricity market price by hour starting at h of the day.     -   {C_(flexible,n)}, n=0, 1, . . . ,55—[$/kWh]—cost of energy for         flexible hydropower generation, corresponding to the n^(th)         interval.

Outputs:

-   -   {P_(G,n)}—[kW]—Total hydropower generation, corresponding to the         n^(th) interval     -   {C_(E,n)}—[$/kWh]—Weighted-average cost of energy for         hydropower, corresponding to the n^(th) interval.

Pseudo Code Implementation:

-   -   1. Restate inputs in the units specified in previous section, if         necessary.     -   2. Generate P_(G,n):         -   Allocate of the scheduled hydropower generation to each             n^(th) interval.

$\begin{matrix} {{\forall n},{P_{G,n} = \left\{ \begin{matrix} {P_{s,{t_{0} + i}},} & {{{if}\mspace{14mu} n} \subseteq \left\lbrack {\left( {t_{0} + i + 1} \right) - \left( {t_{0} + i} \right)} \right\rbrack} \\ {{\frac{1}{\left( {t_{0} + I} \right) - \left( {t_{0} + i} \right)}{\sum\limits_{t = {t_{0} + i}}^{t_{0} + I - 1}\; P_{s,t}}},} & {{{if}\mspace{14mu}\left\lbrack {\left( {t_{0} + I} \right) - \left( {t_{0} + i} \right)} \right\rbrack} \subseteq n} \end{matrix} \right.}} & (1) \end{matrix}$

-   -   -   Make this P_(G,n) prediction available as an output of this             function into the transactive node's algorithmic toolkit             framework.

    -   3. Calculate or update the trend for hour-by-hour electricity         market price that may be used to predict C_(flexible) if better         predictions are not known. For each hour starting at h of the         day, calculate the average electricity market price for the past         7 days. If an implementer possesses better means to make these         predictions, then such predictions should replace this trend         information as it becomes available.

$\begin{matrix} {{\forall h},{C_{{trend},h} = {\frac{1}{7}{\sum\limits_{d = {- 7}}^{- 1}\; C_{m,h,d}}}}} & (2) \end{matrix}$

-   -   -   Successive daily updates may be accomplished as follows:

$\begin{matrix} {{\forall h},{C_{{trend},h} = {\frac{1}{7}\left( {{7 \cdot C_{{trend},h,{old}}} - C_{m,h,{- 8}} + C_{m,h,{- 1}}} \right)}}} & (3) \end{matrix}$

-   -   -   -   C_(trend,h,old)—[$/kWh]—prior value of C_(trend,h) that                 will become displaced by this update.

    -   4. C_(flexible) usually hovers around the electricity market         price. Therefore, it can be predicted by allocating the         electricity market price trend to each n^(th) interval:

$\begin{matrix} {{\forall n},{C_{{flexible},n} = \left\{ \begin{matrix} \underset{\_}{C_{{trend},h_{i}},} & {{{if}\mspace{14mu} n} \subseteq \left( {h_{i + 1} - h_{i}} \right)} \\ {C_{{trend},h},} & {{{if}\mspace{14mu}\left\lbrack {\left( {t_{0} + I} \right) - \left( {t_{0} + i} \right)} \right\rbrack} \subseteq n} \end{matrix} \right.}} & (4) \end{matrix}$

-   -   -   C_(trend,h)—[$/kWh]—Average of all C_(trend,h) between t₀+i             and t₀+l (exclusive).

    -   5. Generate C_(En):         -   Allocate K_(h,s) to each n^(th) interval. Table 38 below is             a lookup table from which K_(h,s) is picked for a given hour             h in a season s.

$\begin{matrix} {{\forall n},{K_{n} = \left\{ \begin{matrix} {\underset{\_}{K_{h_{i},s}},} & {{{if}\mspace{14mu} n} \subseteq \left( {h_{i + 1} - h_{i}} \right)} \\ {K_{h,s},} & {{{if}\mspace{14mu}\left\lbrack {\left( {t_{0} + I} \right) - \left( {t_{0} + i} \right)} \right\rbrack} \subseteq n} \end{matrix} \right.}} & (5) \end{matrix}$

-   -   -   -   K_(h,s) —[unitless]—Average of all K_(h,s) between t₀+i                 and t₀+l (exclusive).

TABLE 38 Lookup table for K_(h, s) S Mar. 21 to June 21 to Sep. 21 to Dec. 21 to June 20 Sep. 20 Dec. 20 Mar. 20 h (Spring) (Summer) (Fall) (Winter) 00:00 10% 10% 10% 10% 01:00  0%  0%  0%  0% 02:00  0%  0%  0%  0% 03:00  0%  0%  0%  0% 04:00  0%  0%  0%  0% 05:00  5% 10% 20%  5% 06:00 10% 20% 20%  5% 07:00 15% 20% 30%  5% 08:00 15% 25% 30% 10% 09:00 15% 25% 40% 15% 10:00 15% 25% 40% 20% 11:00 15% 25% 40% 25% 12:00 15% 25% 40% 30% 13:00 15% 25% 40% 35% 14:00 15% 20% 40% 35% 15:00 15% 10% 40% 35% 16:00 15%  5% 40% 30% 17:00 15%  5% 40% 20% 18:00 15%  5% 40% 20% 19:00 15% 10% 40% 20% 20:00 15% 15% 30% 20% 21:00 15% 20% 20% 20% 22:00 10% 20% 20% 10% 23:00  5% 10% 10% 10%

-   -   Flexible hydropower is traded hourly on the electricity market         by BPA and non-BPA stakeholders. The cost of flexible hydropower         varies not only hourly, but also seasonally and during         short-term events like a heat wave during winter. The cost of         flexible hydropower usually hovers around the current         electricity market price. Further, the values for K_(h,s) given         in the above table are based on the expert opinion of BPA's         hydropower subject matter expert, and not actual historical         hydropower data.         -   Compute C_(E n) as follows:

∀n,C _(E,n)=(1−K _(n))·C _(mustrun) +K _(n) ·C _(flexible,n)  (6)

-   -   -   Make this C_(E,n) prediction available as an output of this             function into the transactive node's algorithmic toolkit             framework.

Subappendix A Hypothetical Supply Stack

FIG. 56 is a graph 5600 of a hypothetical supply stack.

Subappendix B Derivation of C_(E,n)

$\begin{matrix} {{TIS}_{n} = \frac{\begin{matrix} {{{C_{{mustrun},n} \cdot P_{{mustrun},n} \cdot \Delta}\; t_{n}} +} \\ {{{C_{{flexible},n} \cdot P_{{flexible},n} \cdot \Delta}\; t_{n}} + {{all}\mspace{14mu} {other}\mspace{14mu} {costs}}} \end{matrix}}{{{P_{{mustrun},n} \cdot \Delta}\; t_{n}} + {{P_{{flexible},n} \cdot \Delta}\; t_{n}} + {{all}\mspace{14mu} {other}\mspace{14mu} {energies}}}} & \left( {B{.1}} \right) \\ {\left. \Rightarrow{TIS}_{n} \right. = \frac{\begin{matrix} {{{C_{{mustrun},n} \cdot \left( {1 - K_{n}} \right) \cdot P_{G,n} \cdot \Delta}\; t_{n}} +} \\ {{{C_{{flexible},n} \cdot K_{n} \cdot P_{G,n} \cdot \Delta}\; t_{n}} + {{all}\mspace{14mu} {other}\mspace{14mu} {costs}}} \end{matrix}}{\begin{matrix} {{{\left( {1 - K_{n}} \right) \cdot P_{G,n} \cdot \Delta}\; t_{n}} +} \\ {{{K_{n} \cdot P_{G,n} \cdot \Delta}\; t_{n}} + {{all}\mspace{14mu} {other}\mspace{14mu} {energies}}} \end{matrix}}} & \left( {B{.2}} \right) \\ {\left. \Rightarrow{TIS}_{n} \right. = \frac{\begin{matrix} {\left( {{\left( {1 - K_{n}} \right) \cdot C_{{mustrun},n}} + {K_{n} \cdot C_{{flexible},n}}} \right) \cdot P_{G,n} \cdot} \\ {{\Delta \; t_{n}} + {{all}\mspace{14mu} {other}\mspace{14mu} {costs}}} \end{matrix}}{{{P_{G,n} \cdot \Delta}\; t_{n}} + {{all}\mspace{14mu} {other}\mspace{14mu} {energies}}}} & \left( {B{.3}} \right) \\ {\left. \Rightarrow{TIS}_{n} \right. = \frac{{{C_{E,n} \cdot P_{G,n} \cdot \Delta}\; t_{n}} + {{all}\mspace{14mu} {other}\mspace{14mu} {costs}}}{{{P_{G,n} \cdot \Delta}\; t_{n}} + {{all}\mspace{14mu} {other}\mspace{14mu} {energies}}}} & \left( {B{.4}} \right) \\ {{\therefore C_{E,n}} = {{\left( {1 - K_{n}} \right) \cdot C_{{mustrun},n}} + {K_{n} \cdot C_{{flexible},n}}}} & \left( {B{.5}} \right) \end{matrix}$

Subappendix C Examples of C_(E,n)

-   -   Let C_(mustrun)=$0.0035/kWh as shown in Appendix A.     -   In these examples, the sample shown in diagram 5700 of FIG. 57,         which shows Dow Jones Mid-C Hourly Index is used for         C_(trend,h):         -   Note that, although not explicitly written in Figure C1, the             price is in terms of $/MWh.

TABLE 39 Trended electricity market price by the hour h C_(trend, h) [$/kWh]  0:00 0.02083  1:00 0.02330  2:00 0.02231  3:00 0.02272  4:00 0.02773  5:00 0.03443  6:00 0.03356  7:00 0.03489  8:00 0.03493  9:00 0.03583 10:00 0.03276 11:00 0.03276 12:00 0.02859 13:00 0.02859 14:00 0.03010 15:00 0.02507 16:00 0.02228 17:00 0.02762 18:00 0.02898 19:00 0.03047 20:00 0.02603 21:00 0.02071 22:00 0.02945 23:00 0.02097

-   -   Allocate the C_(trend, h) values in Table 39 to each n^(th)         interval to obtain C_(flexible,n,) and the K_(h,s) values to         obtain K_(n) for each season. Then compute C_(E,n) for each         season using equation (6). The outcome is given below in Table         40 and plotted in FIG. 58.

TABLE 40 Examples of the overall cost of energy for hydropower for each season Δt_(n) C_(flexible,n) K_(n) C_(E,n) [$/kWh] n [h] [$/kWh] Spring Summer Fall Winter Spring Summer Fall Winter 0 1/12 0.0208 10% 10% 10% 10% 0.0052 0.0052 0.0052 0.0052 1 1/12 0.0208 10% 10% 10% 10% 0.0052 0.0052 0.0052 0.0052 2 1/12 0.0208 10% 10% 10% 10% 0.0052 0.0052 0.0052 0.0052 3 1/12 0.0208 10% 10% 10% 10% 0.0052 0.0052 0.0052 0.0052 4 1/12 0.0208 10% 10% 10% 10% 0.0052 0.0052 0.0052 0.0052 5 1/12 0.0208 10% 10% 10% 10% 0.0052 0.0052 0.0052 0.0052 6 1/12 0.0208 10% 10% 10% 10% 0.0052 0.0052 0.0052 0.0052 7 1/12 0.0208 10% 10% 10% 10% 0.0052 0.0052 0.0052 0.0052 8 1/12 0.0208 10% 10% 10% 10% 0.0052 0.0052 0.0052 0.0052 9 1/12 0.0208 10% 10% 10% 10% 0.0052 0.0052 0.0052 0.0052 10 1/12 0.0208 10% 10% 10% 10% 0.0052 0.0052 0.0052 0.0052 11 1/12 0.0208 10% 10% 10% 10% 0.0052 0.0052 0.0052 0.0052 12 ¼ 0.0233 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 13 ¼ 0.0233 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 14 ¼ 0.0233 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 15 ¼ 0.0233 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 16 ¼ 0.0223 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 17 ¼ 0.0223 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 18 ¼ 0.0223 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 19 ¼ 0.0223 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 20 ¼ 0.0227 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 21 ¼ 0.0227 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 22 ¼ 0.0227 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 23 ¼ 0.0227 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 24 ¼ 0.0277 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 25 ¼ 0.0277 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 26 ¼ 0.0277 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 27 ¼ 0.0277 0% 0% 0% 0% 0.0035 0.0035 0.0035 0.0035 28 ¼ 0.0344 5% 10% 20% 5% 0.0050 0.0066 0.0097 0.0050 29 ¼ 0.0344 5% 10% 20% 5% 0.0050 0.0066 0.0097 0.0050 30 ¼ 0.0344 5% 10% 20% 5% 0.0050 0.0066 0.0097 0.0050 31 ¼ 0.0344 5% 10% 20% 5% 0.0050 0.0066 0.0097 0.0050 32 1 0.0336 10% 20% 20% 5% 0.0065 0.0095 0.0095 0.0050 33 1 0.0349 15% 20% 30% 5% 0.0082 0.0098 0.0129 0.0051 34 1 0.0349 15% 25% 30% 10% 0.0082 0.0114 0.0129 0.0066 35 1 0.0358 15% 25% 40% 15% 0.0083 0.0116 0.0164 0.0083 36 1 0.0328 15% 25% 40% 20% 0.0079 0.0108 0.0152 0.0094 37 1 0.0328 15% 25% 40% 25% 0.0079 0.0108 0.0152 0.0108 38 1 0.0286 15% 25% 40% 30% 0.0073 0.0098 0.0135 0.0110 39 1 0.0286 15% 25% 40% 35% 0.0073 0.0098 0.0135 0.0123 40 1 0.0301 15% 20% 40% 35% 0.0075 0.0088 0.0141 0.0128 41 1 0.0251 15% 10% 40% 35% 0.0067 0.0057 0.0121 0.0110 42 1 0.0223 15% 5% 40% 30% 0.0063 0.0044 0.0110 0.0091 43 1 0.0276 15% 5% 40% 20% 0.0071 0.0047 0.0131 0.0083 44 1 0.0290 15% 5% 40% 20% 0.0073 0.0048 0.0137 0.0086 45 1 0.0305 15% 10% 40% 20% 0.0075 0.0062 0.0143 0.0089 46 1 0.0260 15% 15% 30% 20% 0.0069 0.0069 0.0103 0.0080 47 1 0.0207 15% 20% 20% 20% 0.0061 0.0069 0.0069 0.0069 48 1 0.0295 10% 20% 20% 10% 0.0061 0.0087 0.0087 0.0061 49 1 0.0210 5% 10% 10% 10% 0.0044 0.0052 0.0052 0.0052 50 6 0.0252 3% 3% 5% 3% 0.0040 0.0042 0.0046 0.0040 51 6 0.0341 14% 23% 33% 13% 0.0078 0.0106 0.0137 0.0076 52 6 0.0270 15% 15% 40% 31% 0.0070 0.0070 0.0129 0.0108 53 6 0.0261 13% 13% 27% 17% 0.0063 0.0065 0.0095 0.0073 54 24 0.0281 11% 14% 26% 16% 0.0062 0.0069 0.0100 0.0074 55 24 0.0281 11% 14% 26% 16% 0.0062 0.0069 0.0100 0.0074

FIG. 58 is a plot 5800 of exemplary overall cost of energy for hydropower for each season.

6.3.10 Load Function Residential Event-Driven Demand Response (Function 2.4) Description

This toolkit function addresses systems of residential demand-response equipment that will be expected to respond relatively infrequently (e.g., perhaps several times per month) to events that will be indicated via the transactive control and coordination system's incentive signal (TIS).

This toolkit function addresses systems that control any combination of (1) residential space heating, (2) residential electric tank water heaters, or (3) smart appliances. Two or more different types of equipment from this list may be grouped into a single asset system and may consequently be described by a single instantiation of this toolkit function. This function allows for multiple response levels. A single asset system uses one single set of thresholds and response levels. If different sets of thresholds (e.g., different demand-response events) should be defined for different types or populations of equipment, then additional functions should be instantiated for each such type or population.

Refer to toolkit load function 2.0 General Event-Driven Demand Response for general guidance and principles that were used during the formulation of this function. The section Pseudo Code Implementation below (and the detailed pseudo code in Subappendix F) lays out the specific calculation strategy and steps of this function.

Block Input/Output Function Model Inputs:

-   -   K_(L)—[dimensionless count]—number of response levels to be         prescribed for this asset system. For example, an asset system         that simply curtails its loads has one response level (e.g.,         “curtailed”).     -   D_(min,L)—[time: minutes]—minimum time duration for which an         event level L should remain in force after it has become         initiated. This duration is also the width of a time window that         will be used for evaluating the magnitude of an event at         level L. Note that if a D_(min,L) is specified, it is         recommended that some D_(x,L) limitation (see two bullets below)         is also specified. This is dictated by equation 8 in the Pseudo         Code Implementation section. If no D_(x) limitation is         specified, there is the risk of an event lasting for undesirable         lengths of time.     -   {N_(this year, L), N_(year, L), N_(this month, L), N_(month, L),         N_(this week, L), N_(week, L), N_(this day, L), N_(day, L),         N_(this hour, L), N_(hour,L)}—[dimensionless count]—local static         input LI_(—)29—limitations on event count or         frequency—constraints that have been placed on the maximum         number of events that may occur for this system of assets during         a given time interval. For example, the set of inputs         {N_(this year, L)=36, N_(this month, L)=6, N_(month, L)=6,         N_(this day, L)=1} specifies that this asset system is permitted         to conduct no more than 36 events over a calendar year, not more         than six being conducted during any calendar month or past         30-day-long period, and not more often than once over a given         calendar day (e.g., period from midnight until midnight) for a         given response level L. Note that if any limitation N_(x,L) is         specified, the corresponding D_(x,L) limitation (see next         bullet) should also be specified. (This is dictated by equation         8 in the Pseudo Code Implementation section. If no D_(x)         limitation is specified, there is the risk of an event lasting         for undesirable lengths of time.)     -   {D_(this year, L), D_(year, L), D_(this month,L), D_(month,L),         D_(this week, L), D_(week, L), D_(this day, L), D_(day,L),         D_(this hour,L), D_(hour, L), D_(this event,L)}—[time duration:         minutes]—local static input LI_(—)30—limitations on curtailment         event duration—constraints that have been placed on the maximum         total duration of events that may endure during a given time         interval.     -   {TIS₀(t), TIS₀(t−5), . . . , TIS₀(t−5k)}—[$/kWh]—recent history         of transactive incentive signals (TIS) by which the statistical         likelihood of various incentive levels will be assessed and         updated. The TIS₀ values from the TIS time series (e.g., the TIS         values that correspond to IST₀) from the last k five-minute         updates are used.     -   {TIS₀, TIS₁, . . . , TIS_(K-1)}—[$/kWh]—current transactive         incentive signal TIS for K future intervals.     -   P_(wh)(t)—[average kW]—typical electrical power consumption by         residential tank water heaters in this region as a function of         time of day. This function may be available as a function or as         a look-up table. See appendix material for an example.     -   OPTIONAL INPUT: {Level, EventStartTime_(L),         EventDuration_(L)}—[Integer, UTC Time, UTC Duration]—records of         events and their durations for events that actually have         occurred at each level L. If this input is unavailable, the         function should infer that events will have occurred every time         that an event response is advised by this function. If this         input is explicitly provided, then the toolkit function can more         accurately know how many events and their durations remain to be         applied into the future.

Interim Calculation Products:

-   -   {DIST_(L)(TIS_(0,min)), DIST_(L)(TIS_(0,min)+Δ$), . . . ,         DIST_(L)(TIS_(0,max)−Δ$)}—[dimensionless]—distributions of         absolute TIS₀ values based on historic TIS incentive signals and         filtered by simple windows of width D_(min,L).     -   {N′_(this year. L), N′_(year, L), N′_(this month, L),         N′_(month, L), N′_(this week, L), N′_(week, L),         N′_(this day, L), N′_(day, L), N′_(this hour, L),         N′_(hour, L)}—[dimensionless count]—cumulative count of actual         events L that have occurred during each of the intervals for         which limitations have been prescribed. Events L may be called         only when the numbers of actual events are fewer than the         numbers of allowed events for relevant intervals in LI_(—)29.         (This set need not be an an explicit input. The function         implementation accepts the responsibility to know how many         events have occurred of each type and how many remain to use in         the future.)     -   {D′_(this year, L), D′_(year, L), D′_(this month, L),         D′_(month, L), D′_(this week, L), D′_(week, L),         D′_(this day, L), D′_(day, L), D′_(this hour, L), D′_(hour, L),         D′_(this event, L)}—[time duration: minutes]—actual cumulative         duration of events of level L at a given point in time. An event         L may be called provided that the event will not cause any total         allowed event duration to exceed the limits established by input         LI_(—)30. (This set need not be an explicit input. The function         implementation accepts the responsibility to know the         accumulated durations of events of each type have occurred and         how much event time remains to use in the future.)

Outputs:

-   -   {ACS₀, ACS₁, . . . , ACS_(K-1)}—[dimensionless]—advisory control         signal for each K future predicted interval. A standardized         approach has been specified by which planned response levels may         be indicated by integer values [−127,127].     -   {ΔL₀, ΔL₁, ΔL_(K-1)}—[kW]—average change in power caused by the         elastic behavior of this asset system for the K future predicted         intervals. The elements of this series will be non-negative         during each future interval for which a response event has been         planned, corresponding to non-zero elements of the asset control         plan.

Pseudo Code Implementation:

-   -   1. Establish/update the statistical distribution s of historical         TIS values. (This process does not require or infer that the         distribution of TIS incentive signals is normal.)         -   a. For each unique D_(min, L), tally the number of times             that the average TIS₀ over interval D_(min, L) falls within             each of a set of bins b, where Δ$ is the size of the bin and             is fixed at $0.001/kWh.

TIS_(0,k,mean)=mean(TIS₀(IST_(0,k) −D _(min,L)<IST₀≦IST_(0,k)))

IF TIS_(0,b)≦TIS_(0,k,mean)<TIS_(0,b)+Δ$, THEN DIST_(L)(TIS_(0,b))=DIST_(L)(TIS_(0,b))+1  (1)

-   -   -   -   TIS_(0,b)—[$/kWh]—lower boundary of distribution                 interval DIST_(L)(TIS_(0,b)), bin b             -   TIS_(0,b)+Δ$—[$/kWh]—upper boundary of distribution                 interval DIST_(L)(TIS_(0,b)), bin b             -   DIST_(L)(TIS_(0,b))—[dimensionless]—a tally count of the                 number of times that the average value of TIS₀ falls                 into the interval bin b over time. (Because the                 distribution will be normalized, it is equally valid to                 sum the durations of the intervals, resulting in a tally                 count of minutes.)

        -   b. Using the distribution for each unique D_(min, L), create             a normalized cumulative distribution φ_(L)(TIS₀) as shown in             equation 2. The interpretation of φ_(L)(TIS₀) is the             fraction of filtered TIS₀ that will be expected to fall in             any of the bins below bin b, inclusive. By subtracting             φ_(L)(TIS_(0,b)) from 1.0, one estimates the fraction of             filtered TIS₀ values that would be greater than             TIS_(0,b)+Δ$.

$\begin{matrix} {{\Phi_{L}\left( {TIS}_{0,b} \right)} = \frac{\sum\limits_{i = {TIS}_{0,\min}}^{{TIS}_{0,b}}\; {{DIST}_{L}(i)}}{\sum\limits_{i = {TIS}_{0,\min}}^{{TIS}_{0,\max} - {\Delta\$}}\; {{DIST}_{L}(i)}}} & (2) \end{matrix}$

-   -   -   -   φ_(L)(TIS₀)—[dimensionless]—normalized cumulative                 distribution of historical averaged TIS₀ values at level                 L.             -   TIS_(0,min)=−$3/kWh and TIS_(0,max)=+$3/kWh

TABLE 41 Useful distribution organization for tracking the distribution of averaged TIS₀ values DIST(TIS₀) φ(TIS_(0, b)) TIS_(0, max) − Δ$ . . . TIS_(0, b) . . . TIS_(0, min) + Δ$ TIS_(0, min)

-   -   A skilled implementer may choose to fit the normalized         cumulative distribution φ(TIS₀) column of Table 41 to a         monotonic function that could be used hereafter instead of this         lookup table. FIG. 59 shows example graphs 5900 for DIST(TIS₀)         and φ(TIS₀).         -   c. For each unique D_(min, L), DIST_(L)(TIS_(0,b)) and             φ_(L)(TIS_(0,b)) may be updated whenever a new series of TIS             becomes available. (One may choose to update DIST(TIS_(0,b))             and φ(TIS_(0,b)) at a time interval of his choice.)     -   2. Update incentive thresholds for this system of assets. The         overall process by which allowed numbers and durations of event         levels will establish thresholds against which future TIS₀         values may be compared is as follows:         -   The TIS threshold at which response level L should be             initiated is the TIS value at which no more than the allowed             event counts N_(L) or allowed total event durations D_(L)             will transpire.         -   This condition is satisfied by the minimum value of             TIS_(0,b) that satisfies all the conditions represented by             equations 3 through 6.         -   For a calendar interval (e.g., those that state “this             interval,” meaning that they are relevant to a given             calendar year, month, week, day, hour), the normalized             cumulative distribution φ_(L)(TIS₀) should meet all             conditions for any defined interval of equations 3 and 4.

$\begin{matrix} {{\Phi_{L}\left( {TIS}_{0} \right)} > {1 - \frac{D_{{{this}\mspace{11mu} x},L}\left( {1 - {N_{{{this}\mspace{11mu} x},L}^{\prime}\text{/}N_{{{this}\mspace{11mu} x},L}}} \right)}{t_{{this}\mspace{14mu} x}^{\prime}}}} & (3) \\ {{\Phi_{L}\left( {TIS}_{0} \right)} > {1 - {{\frac{D_{\min,L}}{t_{{this}\mspace{11mu} x}^{\prime}} \cdot {floor}}\mspace{11mu} \left( \frac{D_{{{this}\mspace{11mu} x},L} - D_{{{this}\mspace{11mu} x},L}^{\prime}}{D_{\min,L}} \right)}}} & (4) \end{matrix}$

-   -   -   -   x={year, month, week, day, hour}             -   t′_(this x)—[time: minutes]—time remaining in calendar                 interval x. For example, at 45 minutes past an hour,                 there are 15 minutes remaining prior to the end of this                 hour interval.             -   floor( )—function that rounds the operand down to the                 next smaller integer.

        -   Constraints that state the maximum number of events and             total duration of events that are permitted during             continuous “trailing” intervals (e.g., within interval             durations that are not aligned with “calendar” months, days,             etc.) create multiple conditions of the types shown in             equations 5 and 6, all of which should be met by a valid             threshold TIS.

$\begin{matrix} {{\Phi_{L}\left( {TIS}_{0} \right)} > {1 - \frac{D_{x,L}\left( {N_{x,L} - N_{x,L}^{\prime}} \right)}{t_{x}}}} & (5) \\ {{\Phi_{L}\left( {TIS}_{0} \right)} > {1 - {{\frac{D_{\min,L}}{t_{x}} \cdot {floor}}\; \left( \frac{D_{x,L} - D_{x,L}^{\prime}}{D_{\min,L}} \right)}}} & (6) \end{matrix}$

-   -   -   -   t_(x)—[time: minutes]—length of the interval over which                 criterion is being addressed (e.g., one year, one month,                 one week, one day, one hour).

        -   The minimum TIS₀ that satisfies equation 3 through 6 for a             response level L is designated as TIS_(threshold, L).

    -   3. Update the advisory control signal time series for this         system of assets. The TIS_(threshold, L) values assessed in the         previous step are now compared to the filtered average current         TIS, which are computed as in equation 7 for each IST interval.         In short, equation 7 says that those TIS intervals that are         shorter than D_(min) are averaged, and those TIS intervals that         are longer than D_(min) are used directly. While this function's         approach is workable without this filtering, the filtering of         equation 7 may help the implementer avoid responding to certain         spurious, short-lived events.

TIS_(filtered,n,L)=mean(TIS_({all n})(IST_(n)≦IST_({all n})<IST_(n) +D _(min,L)))  (7)

-   -   -   If any TIS_(filtered,n,L) exceeds the threshold that was             established in the prior step, an ongoing event has not             lasted D_(min,L), and the allowed event counts N_(L) or             allowed total event durations D_(L) are not exceeded, an             event should be planned for the affected IST intervals, the             event counters and event durations should be updated             accordingly, and an advisory control signal and change in             average power should be planned or predicted for a future             interval n as in equation (8).

IF (TIS_(filtered,n,L)>TIS_(threshold,n,L) OR (D _(event,n,L)≠0 AND D _(event,n,L) <D _(min,L)))

AND (D′ _(this x,n,L) +D _(min,L) −D _(event,n,L) ≦D _(this x,L))

AND (D′ _(x,n,L) +D _(min,L) −D _(event,n,L) ≦D _(x,L))

AND (N′ _(this x,n,L) <N _(this x,L))

AND (N′ _(x,n,L) <N _(x,L))

AND (D′ _(this x,n,L)+(IST_(n+1)−IST_(n))≦D _(this x,L))

AND (D′ _(x,n,L)+(IST_(n+1)−IST_(n))≦D _(x,L))

THEN ACS_(n)=ACS_(L)

ELSE ACS_(n)=unchanged  (8)

-   -   -   Refer to Table 42 that lists the advisory control signals             candidates that may be planned for curtailable loads and             distributed generation according to the numbers of response             levels available from these assets. The algorithm is             complete for this update iteration.

TABLE 42 Example assignable advisory control signals for curtailable load and “dispatchable” distributed generation Number of Advisory Control Response Levels, K_(L) Signals ACS_(L) 1 0 (normal) 127 (curtailed) 2 0 (normal) 64 (level 1) 127 (level 2) 3 0 (normal) 42 (level 1) 84 (level 2) 127 (level 3) 4 Etc.

-   -   4. Predict Change in Average Power that Will Result from         Predicted Demand-Response Control Actions.         -   a. Tank Water Heaters. The typical daily pattern of             electrical energy consumption by residential tank water             heaters may be represented by look-up table or by function             of time of day. A look-up table has been appended. See             Appendix A. The total energy consumption represented in this             table should be represented as inelastic load; when             curtailments are planned, then all or part of the             represented load should be shown as elastic load by this             toolkit function. The magnitudes in the look-up table scale             with the number of tank water heaters represented and             controlled.         -   b. FIG. 60 is a graph 6000 showing a typical water heater             power consumption during week and weekend days.         -   c. Example: A full curtailment of 100 water heaters is             presently planned to occur from 13:50 until 14:30 this             afternoon. Suppose the IST intervals are 5-minutes long             until 14:00 and 15-minutes thereafter. Today is a Tuesday.             First, if the inelastic load from these water heaters has             not already been addressed by another function, the loads in             the weekday columns for the hours of the current IST time             series may be multiplied by 100 and allocated to the IST             intervals that include them. (One may interpolate the values             of 5-minute intervals within the larger 15-minute intervals             found in Subappendix A, but the computational cost of this             incremental improved accuracy may not be worthwhile.) The             water heater control system should be assigned ACS=127 for             these four predicted IST intervals indicating a full             curtailment is planned. Other ACS values will be assigned as             zero. Using Subappendix A, assign ΔL(13:50)=30.8 kW,             ΔL(13:55)=30.8 kW, ΔL(14:00)=33.2 kW, and ΔL(14:15)=31.3 kW.     -   d. Thermostatic Space Conditioning.         -   Input parameters: C, K_(P), U, T_(OSP)(T_(center), K₁, t₁,             K₂, t₂), K_(S), η_(h), η_(c)         -   Other inputs that should be automated by function: K_(DRP),             ΔT_(DRSP), T_(o), P_(S)(I_(ave), t_(sr), t_(ss))         -   A simple dynamic model can be used to predict the energy             that will be consumed by a population of residences             controlled by demand-responsive thermostats or space             conditioning equipment. (This strategy will be so generic             that it should be applicable also to commercial thermostatic             space conditioning loads.) The dynamic model should (1)             predict the inelastic load from the building population             relatively well using few readily available predicted             weather effects like outdoor temperature and solar             insolation, (2) model the first-order dynamics of thermal             energy storage of the building population, (3) approximate             the effects of daily thermostatic occupancy settings and             cycles, (4) accommodate the planning of various             demand-response temperature setbacks and/or power cycling,             and (5) predict with reasonable accuracy any changes in             electrical energy consumption for periods when             demand-response events occur (e.g., the change in elastic             load).         -   This function uses a first-order dynamic model (see             equation 9) for the electrical energy used to heat or cool a             population of buildings. The electrical power is estimated             as the power used to make a representative indoor             temperature track a set point temperature that may be             affected by a pattern of occupancy settings and changes in             the set point that may be caused by demand response. A             single mass is cooled or heated and gains or loses energy             through a representative insulation.         -   The following formulation maintains meanings of many             parameters that will be recognized by buildings experts.             Some, but not all, of the parameters should be scaled to             represent the population of multiple buildings.

$\begin{matrix} {\frac{T_{i}}{t} = {{{- \frac{1}{C}} \cdot \left( {{K_{DRP} \cdot K_{P}} + U} \right) \cdot T_{i}} + {\frac{1}{C} \cdot \left( {{K_{DRP} \cdot {K_{P}\left( {T_{OSP} + {\Delta \; T_{DRSP}}} \right)}} + {U \cdot T_{o}} + {K_{S} \cdot P_{S}}} \right)}}} & (9) \end{matrix}$

$\frac{T_{i}}{t}$

-   -   -   -   —[° C./hour]—rate of change of the representative                 interior temperature T_(i)             -   T_(i)—[° C.]—representative interior temperature             -   C—[kWh/° C.]—effective thermal mass (heat capacity) of                 the building population.             -   Parameter C may be initially estimated based on a rule                 of thumb for wood stick construction and furniture                 contents: 2.0 Btu/° F.-ft² (1.1×10⁻³ kWh/° C.-m²)                 normalized to floor space area. If a typical home has                 150 m² floor area, then the thermal mass of this                 building would be about 0.17 kWh/° C. One thousand such                 homes would have an effective thermal mass of 170                 kWh/° C. An initial estimate may be improved after data                 becomes available for the given modeled building                 population. See Appendix D.             -   U—[kW/° C.]—representative rate of thermal leakage from                 the population of modeled buildings as a function of                 difference between representative interior temperature                 T_(i) and outside temperature T_(o). This number is                 physically based on insulation R-values and total                 building surface areas. An effective estimate might be                 obtained recognizing that virtually all heating and                 cooling energy is eventually lost, in which case this                 parameter is approximately the total energy of space                 conditioning and solar insolation divided by total                 heating and cooling degree-day-hours.             -   Numbers near 0.021 kW/° C. per residential building and                 0.21 kW/° C. per commercial building should be expected,                 so these estimates may be multiplied by the numbers of                 buildings of each type in the modeled population as an                 initial estimate of parameter U. See appendix D.             -   K_(P)—[kW/° C.]—feedback parameter that represents the                 magnitude of heating and cooling equipment power P that                 will be active based on the difference between interior                 temperature T, and its target set point                 T_(OSP)+ΔT_(DRSP). See equation 10. Electrical power                 will be stated in this formulation as a function of                 heating and cooling equipment power P.             -   Until this parameter can be learned from and fit to an                 actual building population, it may be estimated by                 multiplying the number of residential buildings using a                 default value of 0.25 kW/° C. for a residential building                 and perhaps 10 times as much for a commercial building.                 See Appendix D.

P≡K _(DRP) ·K _(P)·((T _(OSP) +ΔT _(DRSP))−T _(i))  (10)

-   -   -   -   K_(DRP)—[dimensionless]—fraction that represents effects                 of demand responses that result in cycling of the space                 conditioning equipment. For example, if a response level                 causes air conditioners to cycle at 50% duty cycle, a                 factor equal to, or more than, 0.5 should be used for                 K_(DRP) while the demand response is active. In                 practice, the effect is less due to oversized equipment,                 and the value of K_(DRP) will be found to be                 considerably larger than the duty cycle. K_(DRP) is                 unity 1.0 at times that no demand response cycling is                 active. This parameter is specific to the thermostat                 program and selected thermostat capabilities. (The duty                 cycle is the fraction of time that the equipment is                 permitted to operate. (That is, unity minus the fraction                 of time the equipment is curtailed.) The parameter                 K_(DRP) is similar to the duty cycle, but it is not                 linearly or functionally related. If a relationship is                 to be stated, declare K_(DRP) as the positive square                 root of the fractional duty cycle D. This relation maps                 D=0 to K_(DRP)=0, and it maps D=1 to K_(DRP)=1. In the                 range D=[0,1], it maps D to a K_(DRP) that is larger                 than D.

D K_(DRP) 0.0 0.0 0.25 0.50 0.50 0.71 0.75 0.87 1.0 1.0

-   -   -   -   T_(OSP)—[° C.]—effective interior temperature setpoint                 that includes the daily effects of occupancy set points.                 This should be set as the representative interior                 temperature that the populations of buildings would                 track as its members move between sleep, away, home, and                 other occupancy settings. See Appendix C for an example                 occupancy temperature setting time series that may be                 used as a starting point for this time series.             -   ΔT_(DRSP)—[° C.]—effective change in interior set point                 temperature for a population of buildings given a                 planned response level. The setback temperature change                 may be identical to an actual temperature setback, but                 it is not necessarily identical to an actual setback.                 This setback temperature is a feature unique to the                 utility program and the capabilities of the vendors'                 products and should be determined for a period during                 which a response level is planned. Temperature changes                 are expected to be plus or minus 1-5° C.             -   T_(o)—[° C.]—representative outdoor temperature that has                 been obtained from the National Weather Data Service or                 similar source. This is an input that should be                 forecasted. It is preferable that this toolkit function                 automate the retrieval of forecasted temperature, using                 coordinates or nearest town or airport as an input.             -   K_(S)·P_(S)—[kW/° C.]—effective total incident solar                 power on the building population, where P_(S) is the                 predicted solar power density [kW/m²], and K_(S) is a                 factor that accounts for the physical characteristics                 and total areas of glazing and other building surfaces.                 See Appendix B for an approach by which this product may                 be predicted for any minute of a day.             -   The electrical power may then be related to the heating                 and cooling power P, taking into account the efficiency                 η of the space-conditioning equipment, as shown in                 equation 11. Heating and cooling power P was defined in                 equation 10. See Appendix E for example scenarios under                 which electrical power has been simulated by this model                 with its default values.

$\begin{matrix} \begin{matrix} {{P_{e} = {\frac{1}{\eta_{h}}{P}}},} \\ {{{if}\mspace{14mu} T_{i}} < {T_{OSP} + {\Delta \; T_{DRSP}}}} \\ {{P_{e} = {\frac{1}{\eta_{c}}{P}}},} \\ {{{if}\mspace{14mu} T_{i}} > {T_{OSP} + {\Delta \; T_{DRSP}}}} \end{matrix} & (11) \end{matrix}$

-   -   -   -   η_(h)—[dimensionless]—effective electrical conversion                 efficiency that relates heating power to expended                 electrical power (default value=1.0).             -   η_(c)—[dimensionless]—effective electrical conversion                 efficiency that relates cooling power to expended                 electrical power (default value=1.3).             -   This formulation has treated state variables and inputs                 as continuous time variables, but one may solve for the                 predicted electrical power for discrete time intervals                 n, provided that the time intervals are short with                 respect to the buildings' thermal response time. (For                 example, discrete time intervals Δt from 1 to 5 minutes                 can be used. Several iterations might be used for IST                 intervals that are longer than 5 minutes. Where an IST                 interval duration is longer than the solution interval                 Δt, the electrical power solutions P_(e) within an IST                 interval should be averaged to obtain the respective                 average inelastic load or elastic change in electrical                 load.) Equation 12 is a discretized version of equation                 9 that may be used to predict the state variable T_(i)                 after each interval n of length Δt.

$\begin{matrix} {{\Delta \; {T_{i}(n)}} = {{\left\lbrack {{{- \frac{1}{C}} \cdot \left( {{{K_{DRP}(n)} \cdot K_{P}} + U} \right) \cdot {T_{i}(n)}} + {\frac{1}{C} \cdot \left( {{{K_{DRP}(n)} \cdot K_{P} \cdot \left( {{T_{OSP}(n)} + {\Delta \; {T_{DRSP}(n)}}} \right)} + {U \cdot {T_{o}(n)}} + {K_{S} \cdot {P_{S}(n)}}} \right)}} \right\rbrack \cdot \Delta}\; {t(n)}}} & (12) \end{matrix}$

-   -   -   -   State variable T_(i)—the representative interior                 temperature—may be updated after each discrete time                 interval using equation 13.

T _(i)(n+1)=T _(i)(n)+ΔT _(i)(n)  (13)

-   -   -   -   The solution should be completed twice: In the first                 case, no demand response is modeled. Both K_(DRP) and                 ΔT_(DRSP) should be set to zero for intervals of case 1.                 The resulting electrical power P_(e) is the inelastic                 load predicted where the space conditioning equipment is                 not responsive to an incentive signal and no demand                 response occurs. In the second case, either or both                 K_(DRP) and ΔT_(DRSP) are assigned for intervals during                 which demand responses have been planned. If demand                 responses have been planned, the solutions for                 electrical power P_(e) will differ by the change in                 elastic load, which is an output of this function                 expected by the toolkit framework.

ΔL _(elastic)(n)=P _(e,case #1)(n)−P _(e,case #2)(n)  (14)

-   -   -   -   e. Smart Appliances and other Loads. The list of                 appliances and devices that could become controlled is                 diverse. Simplifications is necessary until proper                 models of these loads can be completed. The daily                 patterns for plug loads and other devices may be learned                 over time if adequate measurements are being made. Until                 then, hourly load profiles for most of the other                 residential loads are provided in Subappendix H.

Further Alternatives:

-   -   1. The extreme parts of TIS distribution curves should be fitted         to a smooth monotonic function. There is some concern that         event-driven demand response may be allowed so infrequently that         it will be difficult to assign accurate thresholds on TIS.     -   2. While this function has been targeted toward events that         happen at relatively high TIS values, this general approach is         equally valid for infrequent events that occur at very low TIS         values when energy appears to be a great bargain. Today, few         commercially available demand-responsive assets are able to         provide this valuable response.     -   3. The lookup table of Subappendix A is simple to use, but it         does not correctly predict rebound effects that might be         important for peak load management. When water heaters are         released from their curtailed operation, they consume heavily to         reheat the cooled water volume. In some cases, this rebound will         create a new, undesirable peak. Implementers can also use         physics-based models of water heaters that include effects of         thermal energy storage and customer water consumption.         -   An approach similar to the space conditioning model yields             the difference equations 15 and 16 for a population of water             heaters. The parameters of the difference equations may, in             principle, be determined by fitting the modeled             representative water heater power P_(WH)(n) during each             interval n to the observed average power shown in             Subappendix A.

$\begin{matrix} {{T_{WH}\left( {n + 1} \right)} = {{\left( {1 + \frac{{{W(n)} \cdot \Delta}\; {t(n)}}{V_{WH}} - \frac{{{K_{DR}(n)} \cdot K_{P} \cdot \Delta}\; t_{n}}{V_{WH} \cdot C_{W}}} \right) \cdot {T_{WH}(n)}} + {\frac{{{K_{DR}(n)} \cdot K_{P} \cdot \Delta}\; {t(n)}}{V_{WH} \cdot C_{W}} \cdot T_{SP}} - {\frac{{{W(n)} \cdot \Delta}\; {t(n)}}{V_{W}} \cdot T_{i}}}} & (15) \\ {\mspace{79mu} {{P_{WH}(n)} \equiv {K_{P}\left( {T_{SP} - {T_{WH}(n)}} \right)}}} & (16) \end{matrix}$

-   -   -   -   T_(WH)(n)—[ ° C.]—representative temperature of water                 stored in this population of water heaters at the                 beginning of interval n.             -   W(n)—[m³/h]—rate of water consumed by the water heaters                 during interval n.             -   K_(DR)(n)—[dimensionless]—representative impact of a                 demand-response cycling program on the representative                 water heater power P_(WH). At most times, this variable                 is equal to 1.0. During full curtailment of water                 heaters, this variable is equal to 0.0. If the water                 heaters are randomly cycled with an available duty cycle                 of 50%, the this variable would be a number between 0.5                 and 1.0, which number should be determined and fit by                 theory or observation.             -   K_(P)—[kW/° C.]—representative ratio of water heater                 power to the difference between the water heaters'                 representative temperature set point, as is shown in                 equation 16.             -   Δt(n)—[h]—duration of interval n.             -   V_(WH)—[m³]—representative volume of water in the water                 heaters.             -   C_(W)—[kWh/(m³·° C.)]—heat capacity of water.             -   T_(SP)—[° C.]—representative thermostat setpoint for the                 modeled population of water heaters.             -   T_(i)—[° C.]—typical temperature of cold water entering                 the water heaters.

    -   4. The approach to predicting thermostatic space conditioning         loads may be improved in many ways:         -   a. Curve fitting and adaptive algorithms may be developed or             employed to improve the parameters and more accurately model             the given population of buildings. Specifically, the             historical errors between actual and predicted electrical             energy consumption by the population of space conditioning             loads may be used to estimate parameters via least squares.             The parameters to be estimated include constants K_(p), U,             C, and K_(S). Parameter T_(OSP)(t) should be treated as a             function of time-of-day. K_(S)(t) may also be treated as a             function of time-of-day, in which case it might represent             the effects of incident angle of solar power on various             building surfaces.         -   b. Additional inputs may be employed to improve the accuracy             of the model. For example, wind and humidity predictions may             be useful to improve the model's accuracy.         -   c. Higher-order state models may be employed to address             observed dynamic inaccuracies. However, remember that the             states that are meaningful for modeling individual buildings             may not be so useful in an aggregated building model.         -   d. As drafted, the building model is equally applicable to             both heating and cooling. Care should be taken where methods             of heating and cooling are asymmetrical in the modeled             buildings. Different electrical efficiencies η_(h) and η_(c)             have been recommended, but there may also be cause to             distinguish P_(h) and P_(c) if the effective powers of             heating and cooling are different.

SUBAPPENDIX A Daily Water Heater Consumption Patterns for Week and Weekend Days in the Pacific Northwest Time Weekday Weekend (hh:mm) (kW) (kW)  0:00 0.122 0.128  0:15 0.151 0.128  0:30 0.130 0.130  0:45 0.107 0.108  1:00 0.103 0.106  1:15 0.113 0.111  1:30 0.113 0.099  1:45 0.098 0.097  2:00 0.089 0.098  2:15 0.093 0.105  2:30 0.099 0.100  2:45 0.089 0.097  3:00 0.135 0.120  3:15 0.098 0.104  3:30 0.164 0.117  3:45 0.129 0.106  4:00 0.239 0.209  4:15 0.183 0.161  4:30 0.240 0.205  4:45 0.266 0.170  5:00 0.401 0.247  5:15 0.427 0.277  5:30 0.460 0.295  5:45 0.542 0.302  6:00 0.700 0.410  6:15 0.708 0.420  6:30 0.743 0.522  6:45 0.764 0.530  7:00 0.817 0.640  7:15 0.785 0.622  7:30 0.738 0.640  7:45 0.713 0.634  8:00 0.716 0.736  8:15 0.687 0.734  8:30 0.672 0.710  8:45 0.636 0.696  9:00 0.615 0.704  9:15 0.584 0.695  9:30 0.563 0.670  9:45 0.518 0.647 10:00 0.467 0.624 10:15 0.466 0.616 10:30 0.454 0.595 10:45 0.431 0.567 11:00 0.415 0.543 11:15 0.409 0.549 11:30 0.395 0.527 11:45 0.385 0.521 12:00 0.380 0.481 12:15 0.365 0.475 12:30 0.355 0.450 12:45 0.349 0.438 13:00 0.347 0.435 13:15 0.328 0.411 13:30 0.319 0.389 13:45 0.308 0.380 14:00 0.332 0.403 14:15 0.313 0.401 14:30 0.304 0.380 14:45 0.314 0.374 15:00 0.324 0.455 15:15 0.325 0.434 15:30 0.345 0.432 15:45 0.349 0.426 16:00 0.385 0.459 16:15 0.396 0.458 16:30 0.407 0.443 16:45 0.420 0.440 17:00 0.452 0.462 17:15 0.461 0.463 17:30 0.467 0.457 17:45 0.454 0.458 18:00 0.513 0.467 18:15 0.521 0.472 18:30 0.543 0.465 18:45 0.564 0.470 19:00 0.606 0.462 19:15 0.588 0.440 19:30 0.613 0.422 19:45 0.594 0.407 20:00 0.606 0.439 20:15 0.590 0.430 20:30 0.592 0.407 20:45 0.551 0.384 21:00 0.525 0.393 21:15 0.486 0.370 21:30 0.436 0.342 21:45 0.375 0.307 22:00 0.326 0.285 22:15 0.288 0.257 22:30 0.244 0.235 22:45 0.207 0.209 23:00 0.183 0.191 23:15 0.163 0.174 23:30 0.149 0.165 23:45 0.136 0.145

Subappendix B Example Approximation of Effective Incident Solar Power K_(S)·P_(S)

Input parameters: B_(res), B_(com) Inputs that should be obtained automatically by function (e.g., by internet): I_(ave), t_(sr), t_(ss)

The following approach will produce reasonable dynamics to represent the effect of solar insolation on building populations, but it does not rely on actual predicted insolation nor on actual building data and building construction properties. As time permits, this approach may be improved to better predict building performance.

-   -   For the day to be modeled at this location, look up sunrise         (t_(sr))) sunset (t_(ss)), and average insolation (I_(ave)).         This input information may be found at         http://aom.giss.nasa.gov/srlocat.html, for example, if one         enters the month, latitude, and longitude.     -   Estimate K_(S). First estimate the total square meters of         building floor space being modeled. This can be roughly         estimated by multiplying the number of modeled residences by         175, and the number of commercial buildings by 2000. The floor         space will be multiplied by 7% to represent the effects of         glazing and imperfectly reflecting wall and roof surfaces. The         product of floor space and 0.07 extimates K_(S) as shown in         equation B1.

K _(S)=0.07·(175·B _(res)+2000·B _(com))  (B1)

-   -   -   K_(S)—[m²]—estimated effective building surface area that is             exposed to insolation. This parameter accounts for overall             reflectivity, glazing surfaces, and orientation. It is             assumed that this parameter is not a function of time.         -   B_(res)—[count]—number of residential buildings in modeled             population. This is a very gross attempt to scale the impact             of insolation based on the number of buildings and based on             a typical floorspace of represented buildings. The factor             175 may be improved if better information is known about             typical residential buildings that are in this population of             residential buildings.         -   B_(com)—[count]—number of commercial buildings in modeled             population. This is a very gross attempt to scale the impact             of solar insolation based on the number of buildings and             based on a typical floorspace of represented buildings. The             factor 2000 may be improved if better information is known             about typical residential buildings that are in this             population of commercial buildings.

    -   Estimate P_(S)(t). A sinusoidal pattern is assumed for the         insolation through a day between sunrise and sunset.

$\begin{matrix} {{P_{S}(t)} = \left\{ \begin{matrix} {\frac{1440 \cdot I_{ave}}{t_{ss} - t_{sr}} \cdot \left( {1 - {\cos\left( \frac{{\left( {t - t_{sr}} \right) \cdot 2}\pi}{t_{ss} - t_{sr}} \right)}} \right)} & {t_{sr} \leq t \leq t_{ss}} \\ 0 & {otherwise} \end{matrix} \right.} & ({B2}) \end{matrix}$

-   -   -   P_(S)(t)—[W/m²]—insolation as a function of time of day.         -   I_(ave)—[W/m²]—average insolation for this day (over 24             hours/1440 minutes) at this location from             http://aom.giss.nasa.gov/srlocat.html, or similar source of             information. This number is multiplied by the number of             minutes in a day in equation B2 to state the total             insolation expected to be received this day.         -   t—[minutes]—time-of-day represented in minutes. For example,             the time 7:06 should be represented in this function by 426             minutes=7*60+6.         -   t_(sr)—[minutes]—minute of this day on which sunrise occurs.             Care should be taken to address UTC time and daylight             savings properly.         -   t_(ss)—[minutes]—minute of this day on which sunset occurs.             Care should be taken to address UTC time and daylight             savings properly.

An example profile 6100 of P_(s)(t) is shown in FIG. 61 for a day on which t_(sr)—=7:06 (426 minutes into the day), t_(ss)=17:13 (1033 minutes into the day), and I_(ave)=201 W/m².

Subappendix C Example Approach for Smooth Approximation of Occupancy Set Point Temperature TOSP(t)

Input parameters: T_(center), K₁, t₁, K₂, t₂ Input variable: t

The occupancy set point temperature T_(OSP) reflects a representative change in the target interior temperature set point that is induced by building occupants as they schedule or manually change their thermostatic set points for periods of the day. The following approach produces a smooth function of time-of-day while using only a few supplied input parameters.

$\begin{matrix} {{T_{OSP}(t)} = {T_{center} + {K_{1} \cdot {\sin\left( \frac{2{\pi \cdot \left( {t + t_{1}} \right)}}{1440} \right)}} + {K_{2} \cdot {\sin\left( \frac{4{\pi \cdot \left( {t + t_{2}} \right)}}{1440} \right)}}}} & ({C1}) \end{matrix}$

-   -   T_(OSP)(t)—[° C.]—occupancy set point temperature as a function         of time of day t.     -   T_(center)—[° C.]—input temperature to this function that         represents the center of a sinusoidal function. See Table 43 for         example default values.     -   K₁—[° C.]—input parameter that represents a diurnal magnitude of         temperature variation. See Table 43 for example default values.     -   t—[time: minutes]—time of day represented as minutes since the         previous midnight. The number 1440 represents a full day cycle         period of minutes t.     -   t₁—[time: minutes]—input parameter that represents a phase         offset of the diurnal magnitude of temperature variation. See         Table 43 for example default values.     -   K₂—[° C.]—input parameter that represents a magnitude of         temperature variation that occurs at twice the diurnal frequency         (e.g., two full periods per day). See Table 43 for example         default values.     -   t₂—[time: minutes]—input parameter that represents a phase         offset of the diurnal magnitude of temperature variation. See         Table 43 for example default values.

The example input parameters of Table 43 are based on expert opinion and should suffice until data is found to refine these parameters. (It is also acceptable to simply use a constant value T_(center), similar to what has been recommended in Table 43 for summer and fall periods.)

TABLE 43 Five input parameters that may be used to specify the occupancy set point temperature T_(OSP)(t) by season of year T_(center) K₁ t₁ K₂ t₂ (° C.) (° C.) (minutes) (° C.) (minutes) winter 20.0 0.5 −450 0.8 360 spring 21.5 0.0 — 0.0 — summer 23.0 0.2  270 0.5 180 fall 21.5 0.0 — 0.0 —

FIG. 62 is a plot 6200 of a winter profile of T_(OSP)(t) that uses the winter parameters of Table 43. FIG. 63 is a plot 6300 of a summer profile of T_(OSP)(t) that uses the summer parameters of Table 43.

Subappendix D Additional Insights Concerning the Parameters Used to Model Thermostatic Control of Buildings

There are several terms of equation (9) that are useful toward the understanding of relationships between the model parameters.

Thermal Losses

If the effects of space conditioning and solar insolation were eliminated, the relationship of equation D1 would remain and would describe the asymptotic migration of the representative temperature T_(i) toward the ambient outdoor temperature T_(o) that is characterized by the relationship between thermal losses U and thermal mass C.

$\begin{matrix} {\frac{T_{i}}{t} = {\frac{U}{C} \cdot \left( {T_{o} - T_{i}} \right)}} & ({D1}) \end{matrix}$

An insight available from equation D1 is that it defines a relaxation time constant as the ratio C/U. The time constant is the time that it would take for the two temperatures to come within about 37% of the starting difference between the two temperatures. For example, if the interior temperature begins at 20° C. and the outside temperature remains constant at 0° C., the time constant would be the time it takes for the interior temperature to drop to 7.4° C. If that amount of time is estimated to be 8 hours, then the magnitude of parameter C should be 8 times as great as the magnitude of U. Therefore, if the value of C is estimated to be 0.17 kWh/° C. for a residential building, then the value of U should be approximately 0.021 kW/° C., which is the recommended default value for this parameter.

Space Conditioner Size and Responsiveness

If the effects of solar insolation and thermal losses may be temporarily ignored, equation D2 may be derived from equation 9 to represent the rate at which the representative heating or cooling equipment would correct the representative interior temperature T_(i) toward its set point, which is the sum T_(OSP)+ΔT_(DRSP).

$\begin{matrix} {\frac{T_{i}}{t} = {\frac{K_{DRP} \cdot K_{P}}{C}\left( {T_{OSP} + {\Delta \; T_{DRSP}} - T_{i}} \right)}} & ({D2}) \end{matrix}$

In the normal case, K_(DRP) is unity.

Equation D2 is characterized by a time constant as the ratio C/K_(P). Space conditioning equipment is usually sized to correct the interior temperature in a relatively short time. If, for example, buildings heaters were to heat a residential building and its contents from 10° C. to 20° C., it might take about 40 minutes to heat those contents fully to 16.3° C. Therefore, the magnitude of C should be about 0.67 that of K_(P). If C is 0.17 kWh/° C. for a residential building, then the representative magnitude of K_(P) should be about 0.25 kW/° C., which is the recommended default value for this parameter.

Average Electrical Power for Space Conditioning

Another insight may be obtained if one calculated the final, constant condition of how much power it would take to maintain a thermostatic set point for a given outdoor temperature T_(o). One may calculate a final interior temperature T_(i) using equation 9. Then this interior temperature may be used in equations 10 and 11 to predict that resulting electrical power P_(e) that would be consumed.

Ignoring the effects of solar insolation and setting K_(DRP) to unity, the steady-state electrical power is given by equation D3.

$\begin{matrix} {P_{e} = {\frac{1}{\eta} \cdot \left( \frac{U \cdot \left( {T_{OSP} + {\Delta \; T_{DRSP}} - T_{o}} \right)}{1 + \frac{U}{K_{P}}} \right)}} & ({D3}) \end{matrix}$

For present purposes, equation D3 should be used to test the reasonableness of the set of parameters. Given the sets of default parameters recommended so far for a residential building, and assuming efficiency η of the electrical conversion and ΔT_(DRSP) are unity, it would take about 190 average watts to maintain a constant 10° C. difference between the set point and ambient outdoor temperatures in this structure.

Subappendix E Example Electrical Power Profile Cases from Thermostatic Model with Default Winter Parameter

FIG. 64 is a graph 6400 of the predicted electrical power consumption for 1000 thermostatically controlled residential buildings where T_(o)=10° C. FIG. 65 is a graph 6500 of the predicted electrical power consumption for 1000 thermostatically controlled residential buildings where T_(o)=0° C. FIG. 66 is a graph 6600 of the predicted electrical power consumption for 1000 thermostatically controlled residential buildings where T_(o)=0° C.; ΔT_(DRSP)=−2° C. from 8:00 to 10:00 am. FIG. 67 is a graph 6700 of the predicted electrical power consumption for 1000 thermostatically controlled residential buildings where T_(o)=0° C.; K_(DRP)=0.75 from 8:00 to 10:00 am.

Subappendix F: Pseudo Code (for calendar events only) FOR every response level L (excluding L = 0)  [Establish statistical distribution of TIS₀]  Δ$ = $0.001/kWh  TIS_(0,min) = −$3/kWh  TIS_(0,max) = +$3/kWh  Ψ = {TIS_(0,min), TIS_(0,min) + Δ$, TIS_(0,min) + 2 · Δ$, . . . , TIS_(0,max) − Δ$}  FOR all k historical {IST₀, TIS₀} pairs   TIS_(0,k,mean) = mean(TIS₀ (IST_(0,k) − D_(min,L) < IST₀ ≦ IST_(0,k)))     WHERE D_(min,L) is the minimum duration for any event at response level L  END FOR  FOR all k   IF TIS_(0,b) ≦ TIS_(0,k,mean) < TIS_(0,b) + Δ$, WHERE TIS_(0,b) ∈ Ψ THEN    DIST_(L)(TIS_(0,b)) = DIST_(L)(TIS_(0,b)) + 1   END IF  END FOR   $\begin{matrix} {{\Phi_{L}\left( {TIS}_{0,b} \right)} = \frac{\sum\limits_{i = {TIS}_{0,\min}}^{{TIS}_{0,b}}\; {{DIST}_{L}(i)}}{\sum\limits_{i = {TIS}_{0,\min}}^{{TIS}_{0,\max} - {\Delta\$}}\; {{DIST}_{L}(i)}}} & \; \end{matrix}$ END FOR [Initialize iteration index m] m = 0 FOR every new {IST, TIS} series (including relaxation instances):   IF new update interval THEN    m = m + 1   ELSE [IF relaxation instance]    m = m   END IF   IST_({all n},m) = IST_({all n},new series)   TIS_({all n},m) = TIS_({all n},new series)   [Initialize ACS]   ACS{_(all n},m) = 0   FOR every response level L, in ascending order (excluding L = 0)    [Update DIST_(L)(TIS₀) and Φ_(L)(TIS₀)]    TIS_(0,mean) = mean(TIS₀(IST_(0,m) − D_(min,L) < IST₀ ≦ IST_(0,m)))    IF TIS_(0,b) ≦ TIS_(0,mean) < TIS_(0,b) + Δ$, WHERE TIS_(0,b) ∈ Ψ THEN     DIST_(L)(TIS_(0,b)) = DIST_(L)(TIS_(0,b)) + 1    END IF     ${\Phi_{L}\left( {TIS}_{0,b} \right)} = \frac{\sum\limits_{i = {TIS}_{0,\min}}^{{TIS}_{0,b}}\; {{DIST}_{L}(i)}}{\sum\limits_{i = {TIS}_{0,\min}}^{{TIS}_{0,\max} - {\Delta\$}}\; {{DIST}_{L}(i)}}$   FOR intervals n = 0 to 55    [Filter TIS]    TIS_(filtered,n,m,L) = mean (TIS_({all n}) (IST_(n,m) ≦ IST_({all n},m) < IST_(n,m) + D_(min,L)))    IF n = 0 THEN [time space]     IF relaxation instance THEN      D_(event,0,m,L) = D_(event,0,m,L)      D_(this x,0,m,L)′ = D_(this x,0,m,L)′      N_(this x,0,m,L)′ = N_(this x,0,m,L)′       WHERE x = {year, month, week, day, hour} and “this” refers to      calendar periods     ELSE [IF new update interval]      [Update event duration in time space]      IF ACS_(0,m−1) = ACS_(L) THEN       D_(event,0,m,L) = D_(event,0,m−1,L) + (IST_(0,m) − IST_(0,m−1))      ELSE       D_(event,0,m,L) = 0      END IF      [Update calendar x cumulative event duration(s) and count(s) in time     space]      IF change(x, IST_(0,m−1), IST_(0,m)) THEN       D_(this x,0,m,L)′ = 0       N_(this x,0,m,L)′ = 0      ELSE IF ACS_(0,m−1) = ACS_(L) THEN       D_(this x,0,m,L)′ = D_(this x,0,m−1,L)′ + (IST_(0,m) − IST_(0,m−1))       N_(this x,0,m,L)′ = N_(this x,0,m−1,L)′      ELSE IF ACS_(0,m−1) ≠ ACS_(L) AND ACS_(0,m−2) = ACS_(L) THEN       D_(this x,0,m,L)′ = D_(this x,0,m−1,L)′       N_(this x,0,m,L)′ = N_(this x,0,m−1,L)′ + 1      ELSE       D_(this x,0,m,L)′ = D_(this x,0,m−1,L)′       N_(this x,0,m,L)′ = N_(this x,0,m−1,L)′      END IF     END IF    ELSE [IF n = 1 to 55] [future space]     [Update event duration in future space]     IF ACS_(n−1,m) = ACS_(L) THEN      D_(event,n,m,L) = D_(event,n−1,m,L) + (IST_(n,m) − IST_(n−1,m))     ELSE      D_(event,n,m,L) = 0     END IF     [Update calendar x cumulative event duration(s) and count(s) in future space]     IF change (x, IST_(n−1,m), IST_(n,m)) THEN      D_(this x,n,m,L)′ = 0      N_(this x,n,m,L)′ = 0     ELSE IF ACS_(n−1,m) = ACS_(L) THEN      D_(this x,n,m,L)′ = D_(this x,n−1,m,L)′ + (IST_(n,m) − IST_(n−1,m))      N_(this x,n,m,L)′ = N_(this x,n−1,m,L)′     ELSE IF n ≠ 1 AND ACS_(n−1,m) ≠ ACS_(L) AND ACS_(n−2,m) = ACS_(L) THEN      D_(this x,n,m,L)′ = D_(this x,n−1,m,L)′      N_(this x,n,m,L)′ = N_(this x,n−1,m,L)′ + 1     ELSE      D_(this x,n,m,L)′ = D_(this x,n−1,m,L)′      N_(this x,n,m,L)′ = N_(this x,n−1,m,L)′     END IF    END IF    [Determine threshold(s)]     ${\Phi_{\alpha,{{this}\mspace{11mu} x},L} = {1 - \frac{D_{{{this}\mspace{11mu} x},L}\left( {1 - {N_{{{this}\mspace{11mu} x},n,m,L}^{\prime}/N_{{{this}\mspace{11mu} x},L}}} \right)}{t_{{{this}\mspace{11mu} x},n}^{\prime}}}},$     WHERE t_(this x, n)′ = time remaining in this x at every IST_(n, m)     $\Phi_{\beta,{{this}\mspace{11mu} x},L} = {1 - {{\frac{D_{\min,L}}{t_{{{this}\mspace{11mu} x},n}^{\prime}} \cdot {floor}}\mspace{11mu} \left( \frac{D_{{{this}\mspace{11mu} x},L} - D_{{{this}\mspace{11mu} x},n,m,L}^{\prime}}{D_{\min,L}} \right)}}$    TIS_(threshold,n,m,L) = min(TIS₀) such that Φ_(L)(TIS₀) > max(Φ_(α,this x,L), Φ_(β,this x,L))|₀ ¹ for all x    [Determine ACS]    IF (TIS_(filtered,n,m,L) > TIS_(threshold,n,m,L) OR (D_(event, n,m,L) ≠ 0 AND D_(event, n,m,L) < D_(min,L)))     AND (D_(this x,n,m,L)′ + D_(min,L) − D_(event,n,m,L) ≦ D_(this x,L))     AND (N_(this x,n,m,L)′ < N_(this x,L))     AND (D_(this x,n,m,L)′ + (IST_(n+1,m) − IST_(n,m)) ≦ D_(this x,L)) THEN     ACS_(n,m) = ACS_(L)    ELSE     ACS_(n,m) = ACS_(n, m)    END IF   END FOR [every n]  END FOR [every L] END FOR [every new {IST, TIS} series]

In the pseudocode, a set of lower boundaries of bins used to build up TIS₀ distribution.

It is acceptable to use a standard averaging window during initialization. This may be helpful if the initial TIS₀ distribution is to be shared among several load toolkit function implementations at the same transactive node and/or used for response levels L within the same implementation. Note that m is used as an iteration index, so that m−1 refers to the previous update interval. During a relaxation instance, IST₀ remains unchanged. Averaging TIS₀ may have little effect on updating the TIS₀ distribution. If that is the case, the implementer may choose not to do the averaging. This may then allow the update of TIS₀ distribution to be done outside of any single toolkit function implementation to be shared among several toolkit function implementations at the same transactive node and/or used for response levels L within the same implementation. D_(event) is a new variable introduced to keep track of the duration of an event. change(x, t₁, t₂)” represents a function to determine whether calendar period x has changed between t₁ and t₂, inclusive.

Subappendix G MATLAB Simulation Code and Results (for Calendar Events Only) Example 1 One Response Level

Running the above MATLAB code, with K_(L)=1, D_(min,1)=15 min, D_(this day,1)=240 min, N_(this month, 1)=5, D_(this month,1)=5×240 min=1200 min, results in the plots 6800, 6900, 7000, 7100, 7200 of FIG. 68 FIG. 69, FIG. 70, FIG. 71 and FIG. 72 FIG. 73, respectively. Plot 7100 is in time space whereas plot 7200 is in future space.

Example 2 Two Response Levels

Running the above MATLAB code, with

-   -   K_(L)=2, D_(min,1)=120 min, D_(this day,1)=240 min,         N_(this month,1)=5, D_(this month,1)=5×240 min=1200 min,     -   D_(min,2)=15 min, D_(this day,2)=240 min, N_(this month,2)=5,         D_(this month,2)=5×240 min=1200 min,         results in the plots 7300, 7400, 7500, 7600, 7700 of FIG. 73,         FIG. 74, FIG. 75, FIG. 76, and FIG. 77, respectively. Plot 7600         is in time space whereas plot 7700 is in future space.

Subappendix H Typical Hourly Residential Load Profiles

The load profiles in Table 44 are derived from normalized profiles in single-family detached house models found at [H1] and yearly energy consumed by each load computed from equations given in [H2]. Note that 2400 ft² and 4 bedrooms were used to represent a “typical” single-family detached house. In Table 44, MEL refers to miscellaneous electric loads.

-   [H1] U.S. Department of Energy, Building Energy Codes Program,     Residential Prototype Building Models:     http://www.energycodes.gov/development/residential/iecc models -   [H2] U.S. Department of Energy, Building Technologies Program,     Building America House Simulation Protocols, by R. Hendrom and C.     Engbrecht (National Renewable Energy Laboratory), Available at     http://www.nrel.gov/docs/fy11osti/49246.pdf

TABLE 44 Typical Hourly Residential Load Profiles [kW] Cooking Dishwasher Clothes Washer Clothes Dryer Hour Lighting Refrigerator Range Weekday Weekend Weekday Weekend Weekday Weekend MELs 1 0.029 0.0473 0.011 0.0084 0.0090 0.0022 0.0027 0.032 0.039 0.396 2 0.029 0.0463 0.011 0.0037 0.0040 0.0017 0.0021 0.019 0.024 0.365 3 0.029 0.0452 0.006 0.0028 0.0030 0.0009 0.0011 0.013 0.016 0.360 4 0.029 0.0439 0.006 0.0019 0.0020 0.0009 0.0011 0.006 0.008 0.355 5 0.086 0.0432 0.011 0.0019 0.0020 0.0017 0.0021 0.013 0.016 0.342 6 0.179 0.0432 0.017 0.0056 0.0060 0.0026 0.0032 0.019 0.024 0.381 7 0.201 0.0449 0.039 0.0112 0.0120 0.0052 0.0064 0.051 0.063 0.441 8 0.179 0.0473 0.068 0.0169 0.0181 0.0113 0.0138 0.102 0.125 0.468 9 0.079 0.0483 0.073 0.0318 0.0341 0.0170 0.0208 0.156 0.191 0.396 10 0.054 0.0490 0.077 0.0356 0.0381 0.0200 0.0245 0.220 0.270 0.337 11 0.054 0.0473 0.068 0.0309 0.0331 0.0196 0.0240 0.252 0.309 0.345 12 0.054 0.0473 0.079 0.0262 0.0281 0.0174 0.0213 0.263 0.321 0.345 13 0.054 0.0496 0.090 0.0225 0.0241 0.0157 0.0192 0.240 0.293 0.339 14 0.054 0.0496 0.073 0.0253 0.0271 0.0139 0.0170 0.218 0.266 0.351 15 0.054 0.0490 0.070 0.0206 0.0221 0.0122 0.0149 0.196 0.240 0.371 16 0.093 0.0496 0.090 0.0197 0.0211 0.0113 0.0138 0.186 0.227 0.391 17 0.201 0.0523 0.147 0.0206 0.0221 0.0118 0.0144 0.179 0.219 0.463 18 0.279 0.0574 0.239 0.0272 0.0291 0.0113 0.0138 0.175 0.215 0.562 19 0.376 0.0591 0.186 0.0478 0.0512 0.0113 0.0138 0.167 0.204 0.610 20 0.451 0.0574 0.096 0.0609 0.0652 0.0113 0.0138 0.164 0.201 0.630 21 0.459 0.0557 0.056 0.0496 0.0532 0.0113 0.0138 0.169 0.207 0.652 22 0.315 0.0547 0.039 0.0365 0.0391 0.0109 0.0133 0.175 0.215 0.636 23 0.176 0.0523 0.025 0.0243 0.0261 0.0074 0.0091 0.141 0.172 0.551 24 0.072 0.0490 0.017 0.0169 0.0181 0.0039 0.0048 0.077 0.094 0.479

Plots of load profiles given in Table 44 are shown in FIG. 78 through FIG. 84. In particular, FIG. 78 is a plot 7800 of a lighting load. FIG. 79 is a plot 7900 of a refrigerator load. FIG. 80 is a plot 8000 of a cooking range load. FIG. 81 is a plot 8100 of a dishwasher load. FIG. 82 is a plot 8200 of a clothes washer load. FIG. 83 is a plot 8300 of a clothes dryer load. FIG. 84 is a plot 8400 of a miscellaneous electric load.

6.3.11 Load Function Non-Renewable Distributed Generation Event-Driven Demand Response (Function 2.5) Description

This is a function for predicting the responses of distributed generators that only infrequently respond to events that may be identified from an incentive signal. When these assets respond, they transition to a limited number of available response levels, which in this case are limited to two levels—standing idle or generating. This function was adapted quite directly from function 2.4 Residential Event-Driven Demand Response), which includes certain details not repeated here. Also refer to function 2.0 General Event-Driven Demand Response for general guidance about event-driven toolkit functions.

It is assumed that the distributed generator is normally idle, so its inelastic load prediction is zero. If this is not the case, or if the generator is used for objectives other than transactive control, then this toolkit function should be augmented to keep track of its inelastic load as a baseline to its generation under transactive control. Implementers may elect to also keep track of generator availability and scheduled testing periods, which conditions are not being tracked in this function.

This function can respond to absolute or relative TIS as desired by an application. In Version 0.4, a new parameter is recommended to state the effective cost of generation by this distributed generation resource. Because the TIS represents an economic signal, distributed generators that are more expensive than the TIS should not be operated, regardless of how the event periods have been determined.

This function was originally drafted for distributed diesel generators that are operated by UW under the control of operators who (we hope) are responsive to this function's advisory control signal. Having a human in the control loop will affect the reliability of and confidence in the generators' responses, but having a human in the control loop in no other way affected the way that this function was designed. The human operator will introduce uncertainty in the likelihood that advised control actions will be heeded, and this uncertainty may be addressed in future drafts of this function. This function should be useable for most event-driven distributed generators responses.

Block Input/Output Function Model: Inputs:

Inputs for this function are identical to those defined in 2.4 Residential Event-Driven Demand Response with several exceptions listed below. The baseline, inelastic generation is assumed to be the idle state with no power being produced. Therefore, no generation pattern is tracked by this function. If the generator is found to be scheduled for other objectives, the times at which the generators are to be activated should be tracked as a baseline and subtracted from predicted event behaviors.

-   -   P_(DG,L)—[kW]—Power that is expected to be generated at each         response level L. Many generator resources will offer one         response level and will be operated at a single power         level—perhaps the nameplate full power rating of the generator         or generators. Default: 0.0 kW (for each response level).     -   K_(DG)—[cost/energy: $/kWh]—unit cost of generated electrical         energy at which this distributed generation resource is able to         produce electrical energy.     -   K_(L)—[dimensionless count]     -   D_(min,L)—[time: minutes]     -   {N_(this year, L), N_(year, L), N_(this month, L), N_(month, L),         N_(this week, L), N_(week, L), N_(this day, L), N_(day, L),         N_(this hour, L), N_(hour, L)}—[dimensionless count].     -   {D_(this year, L), D_(year, L), D_(this month, L), D_(month, L),         D_(this week, L), D_(week, L), D_(this day, L), D_(day, L),         D_(this hour, L), D_(hour, L), D_(this event, L)}—[time         duration: minutes].     -   {TIS₀(t), TIS₀(t−5), TIS₀(t−5k)}—[$/kWh].     -   { TIS₀, TIS₁, . . . , TIS_(K-1)}—[$/kWh].     -   OPTIONAL INPUT: {Level, EventStartTime_(L),         EventDuration_(L)}—[Integer, UTC Time, UTC Duration].

Interim Calculation Products:

-   -   {DIST(TIS_(0,min)), DIST(TIS_(0,min)+Δ$), . . . ,         DIST(TIS_(0,b)), . . . }—[dimensionless].     -   {N′_(this year, L), N′_(year, L), N′_(this month, L),         N′_(month, L), N′_(this week, L), N′_(week, L),         N′_(this day, L), N′_(day, L), N′_(this hour, L),         N′_(hour, L)}—[dimensionless count].     -   {D′_(this year, L), D′_(year, L), D′_(this month, L),         D′_(month, L), D′_(this week, L), D′_(week, L),         D′_(this day, L), D′_(day, L), D′_(this hour, L), D′_(hour, L),         D′_(this event,L)}—[time duration: minutes].

Outputs:

-   -   {ACS₀, ACS₁, . . . , ACS_(K-1)}—[dimensionless].     -   {ΔL₀, ΔL₁, ΔL_(K-1)}—[kW]. (In this case, the sign convention         should indicate that these are generation resources, not         electrical load.)

Pseudo Code Implementation:

The algorithm by which infrequent events are to be determined from the incentive signal are identical to what was described elsewhere in this application.

-   -   5. Establish/update the statistical distribution of historical         TIS values.     -   6. Update incentive thresholds for this system of assets.     -   7. Determine demand-response event periods for this system of         assets by comparing the transactive control incentive signal         against updated incentive thresholds.     -   8. Update the advisory control signal time series for this         system of assets.     -   9. Predict change in average power that will result from         predicted demand-response control actions. A series of power         generation predicted for each IST interval should be calculated         corresponding to the set of advisory control signals that were         created in the previous step #4.         -   Where no effects of ramp periods or inelastic load patterns             should be modeled, then for each IST_(n) and each response             level L,

ΔL _(L)=

0,

if ACS_(n)=0(no event) or TIS_(n) ≦K _(DG)

max(P _(DG,L)),

if both ACS_(n)≧ACS_(L) and TIS_(n) >K _(DG).  (1)

In simple terms, Equation 1 says that the distributed generators should not operate and their elastic load is zero if either there is no event for time interval n (ACS_(n)=0) or if the TIS for the interval is less than the cost at which the generators can generate (e.g., TIS_(n)≦K_(DG)). If however, the TIS exceeds the cost at which the generators can generate and an event has been determined to invoke one or more response levels (e.g., ACS_(n)≧ACS_(L)), then the amount of power predicted to become generated is the maximum P_(DG,L) for the response levels L that have been invoked.

Further alternatives

-   -   1. Address effects of resource unavailability that may affect         the accuracy of predicted generation during events.     -   2. Address scheduled testing as (potentially) an inelastic         impact as generators are typically tested about an hour or so         each month, regardless of transactive control signals.     -   3. Address the uncertainty of elastic load series elements that         is introduced by human operators.     -   4. If in the future distributed generators are to be modeled         that have ramp-up and ramp-down periods that are comparable to,         or longer than, the update interval of the transactive control         and coordination system, then this function should be extended         accordingly. (The update interval being used in some embodiments         is 5 minutes. If a generator can generate full power within 30         seconds or so, the additional complexity of modeling the ramps         may not be worthwhile.) The effect of the ramp periods is that         the energy produced during the first event intervals, during         which the ramp-up occurs, will produce less energy than         P_(DG,L)×Δt for that interval. Energy may also be produced after         the final event interval while the generators ramp down. See         Appendix A for some calculations that anticipate these ramp-up         and ramp-down periods.

Subappendix A Planning for Ramp-Up and Ramp-Down Periods

This appendix offers some insights about additional considerations, steps, and calculations that should be conducted if a distributed generation resource is found to use ramp-up and/or ramp-down periods that are comparable to, or longer than, the duration of the update interval.

One additional set of inputs is used to indicate whether ramp-up and ramp-down periods are being modeled and their durations:

-   -   {ramp_(on), tr_(on), ramp_(off), tr_(off)}—[Boolean T/F,         minutes, Boolean T/F, minutes]—Input

Boolean indicators that indicate whether the distributed generation resource should be ramped into service (ramp_(on)=“true”) or ramped out of service (ramp_(off)=“true”). If either or both type of ramping is necessary, this function will linearly ramp the predicted power on over tr_(on) minutes and off over tr_(off) minutes. Default: {“false”, 0.0, “false”, 0.0}.

The formulation uses additional sub-steps given the various possible relationships between tr_(on), tr_(off), and the IST_(n) times. In certain embodiments, IST_(n*) is defined as the IST_(n) at which an event and tr_(on) are initiated. In some embodiments, IST_(n**) is defined as the IST_(n) that immediately follows the event. If it were not for tr_(off), no power would be generated in the interval that starts at IST_(n**).

The approach will be to define generation power p_(n) at each time IST_(n). Then, the average power may be determined from these points and knowledge of the ramp rates. FIG. 85 is a block diagram 9500 showing an example model of ramp up and ramp down periods.

-   -   First, if

IST_(n)≦IST_(n*), or if IST_(n)≧IST_(n**) +tr _(off),  (A1)

-   -   then p_(n)=0.     -   However, if IST_(n) falls within the interval

IST_(n*)≦IST_(n)≦IST_(n**) +tr _(off),  (A2)

-   -   then assign p_(n) as shown in equation A3.

$\begin{matrix} {p_{n} = {\min\left( {\frac{\left( {{IST}_{n} - {IST}_{n^{*}}} \right) \cdot P_{{DG},L}}{{{ramp}_{on} \cdot {tr}_{on}} + ɛ},P_{{DG},L},{\left( {1 - \frac{\left( {{IST}_{n} - {IST}_{n^{**}}} \right.}{{{ramp}_{off} \cdot {tr}_{off}} + ɛ}} \right) \cdot P_{{DG},L}}} \right)}} & ({A3}) \end{matrix}$

-   -   The small positive value epsilon has been used in the         denominators to avoid division by zero, which could otherwise         have occurred if either the Boolean operators ramp_(on) or         ramp_(off) were false (e.g., “0”) or if either of the ramp         durations tr_(on) or tr_(off) were zero.     -   Now that generation power has been determined at each time         IST_(n), the average power over each IST interval should be         estimated, where ramping may at times reduce the estimate.     -   For any interval prior to IST_(n), or after IST_(n**)+tr_(off),         ΔL_(n)=0. No power is produced by the distributed generators         during these intervals.     -   For any interval IST_(n) that coincides with or follows IST_(n*)         and also starts before IST_(n**)+tr_(off), then

$\begin{matrix} \begin{matrix} {0,} & \begin{matrix} {p_{n} = 0} \\ {p_{n + 1} = 0} \end{matrix} \\ {{0.5*\left( {p_{n} + p_{n + 1}} \right)},} & \begin{matrix} {p_{n} < P_{{DG},L}} \\ {p_{n + 1} < P_{{DG},L}} \end{matrix} \\ {{{\Delta \; L_{n}} = \frac{{p_{n + 1} \cdot \left( {{IST}_{n + 1} - {IST}_{n} - {\frac{{tr}_{on}}{2} \cdot \left( {1 - \frac{p_{n}}{p_{n + 1}}} \right)}} \right)} + {p_{n} \cdot \frac{{tr}_{on}}{2} \cdot \left( {1 - \frac{p_{n}}{p_{n + 1}}} \right)}}{{IST}_{n + 1} - {IST}_{n}}},} & \begin{matrix} {p_{n} < P_{{DG},L}} \\ {p_{n + 1} = P_{{DG},L}} \end{matrix} \\ {\frac{{p_{n} \cdot \left( {{IST}_{n + 1} - {IST}_{n} - {\frac{{tr}_{off}}{2} \cdot \left( {1 - \frac{p_{n + 1}}{p_{n}}} \right)}} \right)} + {p_{n + 1} \cdot \frac{{tr}_{off}}{2} \cdot \left( {1 - \frac{p_{n + 1}}{p_{n}}} \right)}}{{IST}_{n + 1} - {IST}_{n}},} & \begin{matrix} {p_{n} = P_{{DG},L}} \\ {p_{n + 1} < P_{{DG},L}} \end{matrix} \\ {P_{{{DG}.},L},} & \begin{matrix} {p_{n} = P_{{DG},L}} \\ {p_{n + 1} = P_{{DG},L}} \end{matrix} \end{matrix} & ({A4}) \end{matrix}$

6.3.12 Incentive Function Fossil Generation (Function 3.0) Description

This function provides the predict fossil generation and its cost aggregated for each transmission zone.

The cost for generating fossil energy includes a fixed infrastructure cost and a variable production cost. The infrastructure cost will be based on estimated amortized fossil heneration plant infrastructure expense; while the variable production cost is mainly based on fuel cost.

Fossil generators include the following types:

-   -   Nuclear     -   Coal     -   Geothermal     -   Natural Gas Combined Cycle

For simplicity, the infrastructure cost will be calculated for each of the above categories of generation based on the average capital cost provided in Subappendix B in (kaplan 2008).

Coal: 2519 $/kw Nuclear: 3930 $/kw Geothermal: 3170 $/kw Natural Gas Combined Cycle: 1165 $/kw

The infrastructure cost of a fossil generating unit can thus be estimated if its capacity is known. This cost shall then be spread over the lifetime T of the generating unit.

It is permissible for the implementer of this function to assume that T=8760 (h/year)*40 (years)*0.9 (utilization factor)=315360 (hours) if better estimates are unavailable for the lifetime of fossil generating unit.

It is unlikely that any of the fossil units will surpass their stated lifetime in the short-time. However, after a generating unit exceeds its planned lifetime, a decision should be made. Thereafter, the infrastructure cost may be (a) zeroed out, (b) replaced by ongoing maintenance costs, or (c) continued as before as an ongoing replacement cost. This function should be revisited and refined when this situation will be encountered.

The generating units available to meet system load are “dispatched” (put on-line) in order of lowest variable cost. This is referred to as the “economic dispatch” of a power system's plants. For a plant that uses combustible fuels (such as coal or natural gas) a key driver of variable costs is the efficiency with which the plant converts fuel to electricity, as measured by the plant's “heat rate.” This is the fuel input in British Thermal Units (btus) used to produce one kilowatt-hour of electricity output. A lower heat rate equates with greater efficiency and lower variable costs.

A Unit Commitment and Dispatch Engine is used to produce generation MW, that can meet BPA load forecast. Generation cost is calculated based on the heat rate curves and fuel prices.

FIG. 86 is a block diagram 8600 of a block input/output function model, which is discussed below.

Inputs:

-   -   Predicted price of fuel, which may be either constant or a         dynamic time series, depending on the fuel.     -   Representative amortized infrastructure cost. (In some cases,         the infrastructure costs will be stated as functions of many         variables, including local costs of money, taxes, regulations,         etc.)     -   Planned generator schedule(s), such as Federal hydro schedules.     -   Constant heat rate curves of fossil generators.     -   BPA Load Forecast.     -   Historical BPA Netmom savecases, which are used to produce         generation and load profiles for any given hour of a day in a         week of a specific season.     -   Amortized Infrastructure cost C_(I,G)

Outputs:

-   -   Predicted average generated fossil power P_(TZ,IST) For a         Transmission Zone TZ for time series using the intervals of the         current IST time series.     -   Corresponding predicted energy costs of generated power in each         transmission zone C_(E,TZ,IST) using the intervals of the         current IST time series.     -   Predicted infrastructure cost in each transmission zone         C_(t,TZ,IST) time series using the intervals of the current IST         time series. (Infrastructure cost is not expected to be         especially dynamic, but it is specified as a time series for         consistency.)

Pseudo Code Implementation:

-   -   1. Process inputs from BPA;     -   2. Complement input data with the model data from historical         Netmom savecases and WECC heat rate curves;     -   3. Solve a multi-interval economic dispatch problem which         produces dispatch MW for each generator P_(G,t)     -   4. Calculate P_(TZ,IST) for transmission Zone TZ and interval         IST;

${P_{{TZ},{IST}} = {\sum\limits_{\underset{GisFossil}{G \in {TZ}}}\; P_{G,t}}},$

where t is covers the majority portion of an IST interval

-   -   5. Compute the infrastructure cost C_(I,TZ,IST) corresponding to         each transmission zone TZ and each IST;

$C_{I,{TZ},{IST}} = {\sum\limits_{G \in {TZ}}\; \left( {{\sum\limits_{{Gis}\; {Gas}}\; {C_{I,{Gas}}*T_{IST}}} + {\sum\limits_{{Gis}\; {Geo}}\; {C_{I,{Geo}}*T_{IST}}} + {\sum\limits_{{Gis}\mspace{11mu} {Coal}}\; {C_{I,{Coal}}*T_{IST}}} + {\sum\limits_{{Gis}\; {Nuke}}^{\;}\; {C_{I,{Nuke}}*T_{IST}}}} \right)}$

-   -   6. Compute the energy product cost C_(E,TZ,IST) for each         transmission zone TZ and each IST;

${C_{{TZ},{IST}} = {\sum\limits_{\underset{{Gis}\; {Fossil}}{G \in {TZ}}}\; {C_{G,t}\text{/}T_{t}*T_{IST}}}},$

where t is covers the majority portion of an IST interval

6.3.13 Load Function Residential Time-of-Use Demand Response (Function 3.4) Description

This function predicts the response from an automated residential demand-response system that will respond approximately daily to help mitigate peak conditions that are evident in an incentive signal. The peak period will be based on response constraints and the TIS incentive signal. (Note that this approach is more dynamic than typical time-of-use (TOU) demand response, in which daily peak and off-peak intervals remain immutable. The peak and off-peak periods recommended by this function may be assigned differently each day according to events that will have affected the predicted TIS incentive signals.) It may be applied where programmable, communicating thermostats; smart appliances, demand-response switch units, or other assets are installed in residences and where programs are designed to have these systems respond to daily peak periods.

In some cases, this function will be used by the asset systems IF-04 (water heater control), IF-08 (thermostat load control), and LV-02 (water heater demand-response units). (This document may be useful for the determination of appropriate daily intervals, but a unique function may be used to predict the changes in elastic load from such a diverse and changing population of responsive assets.)

A first objective of this function is to establish the time periods during which the response level(s) should be called, based upon the numbers and durations and preferred durations of these periods that are permitted for each response level. The daily events and their durations are positioned to best align with the levels of the TIS incentive signal that has been predicted for the day.

The function should then predict the change in load that will result from these events having been planned. This toolkit function addresses systems that control any combination of (1) residential space heating, (2) residential electric tank water heaters, or (3) smart appliances. Relatively simple models of populations of these devices are used to predict the changed load that they will consume as they respond to these various peak periods.

Block Input/Output Function Model: Inputs:

-   -   L—[dimensionless count]—number of response levels to be         prescribed for this asset system. For example, an asset system         that simply curtails its loads has one response level (e.g.,         “curtailed”), so L=1.     -   {Threshold₁, Threshold₂, . . . , Threshold_(l), . . . ,         Threshold_(L)}—[dimensionless fraction]—typical fraction of time         that each response level/should be active during a day. For         example, if a system with two response levels has its highest         level designed to respond during the two worst peak hours of a         day, then Threshold₂=2/24=0.083. If the first level may include         an additional 2 hours in its peak period, then         Threshold₁=4/24=0.17. In this example, the system would be in         its normal, non-responding condition for         1−Threshold₁−Threshold₂=0.75. (Through this formulation, it will         be assumed that the thresholds are ordered in increasing order,         from least to greatest.) (Default={1/(L+1), 2/(L+1), . . . ,         l/(L+1), . . . , L/(L+1)})     -   {D_(min,week day,l), D_(min,weekend day,l),         D_(min,holiday,l)}—[time: minutes]—for each response level l,         minimum time duration for which an event level l should remain         in force for this day type after it has become initiated. (In         some cases, this can be stated in multiples of 5, 15, 60, or 360         minutes to align with the IST interval durations.) (Default={15         minutes, 15 minutes, 15 minutes})     -   {N_(min,week day,l), N_(min,weekend day,l),         N_(min,holiday,l)}—[dimensionless count]—local static input         LI_(—)29—limitations on the minimum number of TOU events that         may be called during the three major day types for each response         level l. (Default={0, 0, 0})     -   {N_(max,week day,l), N_(max,weekend day,l),         N_(max,holiday,l)}—[dimension less count]—local static input         LI_(—)29—limitations on the maximum number of TOU events that         may be called during the three major day types for each response         level l. (Default={1, 0, 0})     -   {D_(max,week day,l), D_(max,weekend day,l), D_(max,holiday,l),         D_(max event,l)}—[time duration: minutes]—local static input         LI_(—)30—maximum total event duration permitted per day type and         per event allowed for each event level l—constraints that have         been placed on the maximum total duration of events that may         endure during a day type or during an event. (In some cases,         this can be stated in multiples of 5, 15, 60, or 360 minutes to         align with the IST interval durations.) (Default={1440 minutes,         1440 minutes, 1440 minutes} (e.g., no limit))     -   {TIS₀(t), TIS₀(t−5), TIS₀(t−5k)}—[$/kWh]—recent history of         transactive incentive signals (TIS) by which the statistical         likelihood of various incentive levels will be assessed and         updated. The TIS₀ values from the TIS time series (e.g., the TIS         values that correspond to IST₀) from the last k five-minute         updates are used. (It should be allowed that a recursive method         be initiated, in which case historical TIS₀ data may not be         needed. If historical TIS₀ is not used, system responses should         initially be canceled or more conservatively applied until the         recursive method has learned a meaningful statistical         distribution of the TIS signals.)     -   {TIS₀, TIS₁, . . . , TIS_(K-1)}—[$/kWh]—current transactive         incentive signal TIS for future IST intervals.     -   P_(wh)(t)—[average kW]—typical electrical power consumption by         residential tank water heaters in this region as a function of         time of day. This function may be available as a function or as         a look-up table. See appendix material for an example.

Interim Calculation Products:

-   -   {DIST(TIS_(0,min)), DIST(TIS_(0,min)+Δ$), . . . ,         DIST(TIS_(0,b)), . . . }—[dimensionless]—distributions of         absolute TIS₀ values based on historic or monitored TIS         incentive signals.     -   {φ(TIS_(0,1)), φ(TIS_(0,2)), . . . , φ(TIS_(0,b)),         φ(TIS_(0,B))}—[dimensionless fraction]—cumulative distribution         of historical TIS₀ values. (This will sometimes be abbreviated         as φ(b), where b is the bin that is lower bounded by TIS_(0,b).)

Outputs:

-   -   {ACP₀, ACP₁, . . . , ACP_(K-1)}—[dimensionless]—asset control         plan for each future predicted interval. A standardized approach         has been specified by which planned response levels may be         indicated by integer values [−127,127].     -   {{ΔL₀, ΔL₁, . . . , ΔL_(K-1)}—[kW]—average change in power         caused by the elastic behavior of this asset system for future         predicted intervals. The non-zero elements of this series         corresponding to non-zero elements of the asset control plan.         (Positive values are used here to refer to additional power that         is made available to the system by curtailed loads.)

Pseudo Code Implementation:

-   -   1. Establish/update the statistical distribution of historical         TIS₀ values. (This general process does not require that the         distribution of TIS incentive signals is a normal distribution.)         -   a. Using available historical information and the TIS time             series that becomes available to the transactive node at an             update interval, create a distribution of bins b that are             Δ$-wide for the available TIS₀ values. (Bins of size             Δ$=$0.001 are probably small enough for this function.) For             each available TIS₀,

If TIS_(0,b)≦TIS₀<TIS_(0,b)+Δ$, then set DIST(TIS_(0,b))=DIST(TIS_(0,b))+1  (1)

-   -   -   -   TIS_(0,b)—[$/kWh]—lower boundary of distribution                 interval DIST(TIS_(0,b)), bin b             -   TIS_(0,b)+Δ$—[$/kWh]—upper boundary of distribution                 interval DIST(TIS_(0,b)), bin b             -   DIST(TIS_(0,b))—[dimensionless]—a tally count of the                 number of times that TIS₀ have fallen into the interval                 bin b over time. (Because the distribution will be                 normalized, it is equally valid to sum the durations of                 the intervals, resulting in a tally count of minutes.)

        -   c. Using DIST(TIS₀), create a normalized cumulative             distribution φ(b) as shown in equation 2. The interpretation             of φ(b) is the fraction of TIS₀ that will be expected to             fall in any of the bins below bin b, inclusive. By             subtracting φ(b) from 1.0, one estimates the fraction of             TIS₀ values that would be expected to be greater than             TIS_(0,b)+Δ$. Refer to Table 45 and FIG. 87, which is a set             8700 of plots for DIST(TIS₀) and φ(b).

$\begin{matrix} {{\Phi (b)} = \frac{\sum\limits_{{bin}\mspace{11mu} 0}^{{bin}\mspace{11mu} b}\; {{DIST}\mspace{14mu} \left( {TIS}_{0} \right)}}{\sum\limits_{{bin}\mspace{11mu} 0}^{{bin}\mspace{11mu} B}\; {{DIST}\mspace{11mu} \left( {TIS}_{0} \right)}}} & (2) \end{matrix}$

-   -   -   -   φ(b)—[dimensionless fraction in the range                 [0,1]]1—normalized cumulative distribution of historical                 TIS₀ values in bins 0 through b, inclusive.             -   Bin 0—[$/kWh]—bin that possesses the smallest TIS₀ value                 that can be found in DIST(TIS₀).             -   Bin B—[$/kWh]—bin that possesses the largest TIS₀ value                 that can be found in DIST(TIS₀).

TABLE 45 Useful table for tracking the distribution of historical TIS₀ values DIST(TIS₀) φ(b) Bin B . . . Bin b . . . Bin 1 Bin 0

-   -   -   A skilled implementer may choose to fit the normalized             cumulative distribution φ(b) column of Table 45 to a             monotonic function that could be used hereafter instead of             this lookup table.         -   DIST(TIS₀) and φ(b) may be updated whenever a new TIS₀             becomes available. (One may choose to update DIST(TIS₀) and             φ(b) at a time interval of his choice. Some seasonal             variation in the distribution should be anticipated.             Therefore, it is advised the distribution be established             representative of this month or this season.)

    -   2. Update incentive thresholds for this system of assets. This         step refers to the set {Threshold_(l)} to establish the typical         fraction of a day and TIS values for which a TOU event should be         active for a given response level l.

The value TIS_(0,b) in equation 3 is an acceptable threshold TIS_(thresh,l) for future TIS values and response level l if the condition of equation 3 is true. (One may interpolate to find a better threshold value.) Determine an acceptable threshold for each response level/using equation 3.

Φ(b)≦1−Threshold_(l)<Φ(b+1)  (3)

-   -   3. Calculate averaged TIS values from the thresholds and         statistical information. The raw threshold values are not as         useful as averaged TIS values for given response levels. For         each response level l, calculate the average of the TIS values         that are expected to fall greater than the level's threshold.

$\begin{matrix} {{\overset{\_}{TIS}}_{{thresh},l} \equiv \frac{\sum\; {{\left( {{TIS}_{0,b}^{*} + {0.5 \cdot {\Delta\$}}} \right) \cdot {DIST}}\mspace{11mu} \left( {TIS}_{0,b}^{*} \right)}}{\sum\; {{DIST}\mspace{11mu} \left( {TIS}_{0,b}^{*} \right)}}} & (4) \end{matrix}$

-   -   -   TIS _(thresh,l)—[$/kWh]—average of TIS values expected to be             greater than TIS_(thresh,l).         -   TIS_(0,b)*+0.5 Δ$—[$/kWh]—the center of any DIST(TIS_(0,b))             bin b that holds values greater than or equal to             TIS_(thresh,l).         -   DIST(TIS_(0,b)*)—[dimensionless count]—the count of members             in DIST(TIS_(0,b)) bin b that hold values greater than or             equal to TIS_(thresh,l).

    -   4. Determine TOU event periods for this system of assets.         -   a. An initial calculation of candidate TOU periods is             completed to find periods of time during which the average             predicted TIS_(n) will be greater than or equal to TIS             _(thresh,l). In general, candidate TOU response periods for             response level l are the sets of IST intervals from IST_(n)             to IST_(n+m) (whole numbers m=0, 1, . . . ) that             simultaneously maximize the left-hand side of inequality 5             while also satisfying inequality 6.

$\begin{matrix} {{\frac{\sum\limits_{n^{*}}^{n^{*} + m^{*}}\; {{TIS}_{n} \cdot \left( {{IST}_{n + 1} - {IST}_{n}} \right)}}{\sum\limits_{n^{*}}^{n^{*} + m^{*}}\; \left( {{IST}_{n + 1} - {IST}_{n}} \right)} \geq {\overset{\_}{TIS}}_{{thresh},l}},} & (5) \end{matrix}$

-   -   -   -   where

IST_(n*+m*+1)−IST_(n*) ≦D _(max,l).  (6)

-   -   -   -   -   n*—[dimensionless]—specific index n of the current                     IST_(n) intervals that both maximizes the left-hand                     side of inequality 5 and satisfies inequality 6 to                     define a TOU period for response level l.                 -   m*—[dimensionless index]—whole number index that                     combined with n* maximizes the left-hand side of                     inequality 5 and satisfies inequality 6 to define a                     TOU period for response level l.                 -   IST_(n*+m*+1)—[time in UTC]—ending time of the newly                     defined TOU period.                 -   IST_(n*)—[time in UTC]—beginning time of the newly                     defined TOU period.                 -   D_(max,l)—[time: minutes]—relevant maximum period                     duration or durations for this day or event selected                     from the defined input set                     {D_(max,week day,l)D_(max,weekend day,l),                     D_(max, holiday,l) D_(max event,l)}.

            -   If more than one response level is being used (e.g.,                 L>1), then this step will likely have defined nested                 response periods where the periods of response level 1                 are nested within periods of response level 2, and so                 on. Normally, the hierarchy or priority of these nested                 response periods will be trivial, such that the response                 periods with smaller response level l trump those of                 greater l.

            -   There are L+1 total levels. The remaining level L+1 will                 most often, but not necessarily, be assigned to normal                 operation, unmodified by the TIS.

            -   Because the IST intervals gradually change from granular                 to coarse into the future, the function might see                 response levels over or under prescribed far into the                 future when coarse 6-hour or daylong IST intervals have                 been specified. These conditions should disappear as                 representations become finer and may be mitigated, to a                 degree, by the input assignments of maximum allowed TOU                 period durations.

        -   b. Special case: The number of defined events is more than             the maximum number allowed for a given day. If for any             response level l and day type there have been defined a             number of TOU periods and their respective n* indices within             a day that exceeds the relevant limit from the defined input             set {N_(max,week day,l), N_(max,weekend day,l),             N_(max,holiday,l)) then those periods within the day having             the least (lesser) of the magnitudes on the left-hand side             of inequality 5 should be discarded.

        -   c. Special case: The number of defined events is less than             the minimum number allowed for a given day and its day type.             If for any response level l and day type there has been             defined a number of TOU periods and their respective n             indices within the day fewer than the minimum number of             response periods allowed by the input set             {N_(min,week day,l), N_(min,weekend day,l)N_(min,holiday,l))             then inequality 6 and inequality 7 should be relaxed             somewhat as shown in inequalities 7 and 8. The corrected             indices n* and m*will yield an acceptable number of event             periods for this response level l and day type. The             corrected indices n* and m* are those that solve             inequalities 7 for the smallest positive real number δ for             which the minimum allowed TOU period duration of inequality             8 is achieved.

$\begin{matrix} {{\frac{\sum\limits_{n^{*}}^{n^{*} + m^{*}}\; {{TIS}_{n} \cdot \left( {{IST}_{n + 1} - {IST}_{n}} \right)}}{\sum\limits_{n^{*}}^{n^{*} + m^{*}}\; \left( {{IST}_{n + 1} - {IST}_{n}} \right)} \geq {{\overset{\_}{TIS}}_{{thresh},l} - \delta}},} & (7) \end{matrix}$

-   -   -   -   where

D _(min,l)≦IST_(n*+m*+1)−IST_(n*) ≦D _(max,l).  (8)

-   -   -   -   -   δ—[$/kWh]—smallest positive real value for which                     satisfactory indices n* and m* exist.                 -   D_(min,l)—[time: minutes]—minimum TOU period                     duration allowed for this day type and response                     level as selected from defined inputs {D_(min,1),                     D_(min,2), . . . , D_(min,l), . . . , D_(min,L)}.

            -   One may revisit inequalities 7 and 8 until the minimum                 allowed numbers of events have been defined.

    -   5. Specify the prioritization of response levels. Because the         TOU periods will have been assigned nested one inside another,         the designer should specify the prioritization or hierarchy of         the assigned response levels.         -   a. Example 1: Curtailment using one response level. One can             start with one of the simplest cases. Suppose that a             controlled electrical load will be curtailed during response             level 1 and behave normally otherwise. The prioritization of             the response levels here is trivial as shown in Table 46.             (The advisory control signal column “ACS” in this table will             be discussed in the next section.)

TABLE 46 Response-Level Prioritization for Curtailment Example Response Levels Priority Assigned to IST_(n) Assignment Action/Outcome ACS 1 1 Curtailed system 127 operation none none Normal operation 0

-   -   -   b. Example 2: Five-level TOU battery system. As the number             of response levels and complexity of the controlled asset             system increases, the challenge of prioritizing the response             levels increases, too. Refer to Table 47, which defines the             priority of assignments to be made for a battery system that             has four response levels available to it. (Those who are             familiar with battery storage will correctly recognize that             a battery system will have additional constraints that may             be managed either implicitly or explicitly.) This example             has the additional complexity from a storage system that can             either increase the available power (ACS>0) or decrease the             available power (ACS<0) at its transactive node.

TABLE 47 Response-Level Prioritization for a Battery System with Five Available Response Levels Response Levels Priority Assigned to IST_(n) Assignment Action/Outcome ACS 1, 2, 3, and 4 1 Maximum Charge Bias −127 Strategy 2, 3 and 4 2 Moderate Charge Bias −64 Strategy 3 and 4 3 Inactive Dead Zone 0 4 4 Moderate Discharge Bias 64 Strategy none remaining level Maximum Discharge Bias 127 Strategy

-   -   6. Update the advisory control signal time series for this         system of assets. Advisory control signals are discussed         elsewhere in this application. In short, an advisory control         signal should be stated for an IST interval n and will be         non-zero for any interval during which a response other than         normal operation is planned. Refer to Table 48 that lists the         advisory control signals candidates that will typically be sent         for curtailable loads and distributed generation according to         the numbers of response levels available from these assets.

TABLE 48 Recommended assignable advisory control signals for curtailable load and “dispatchable” distributed generation Number of Advisory Control Response Levels Signals ACS_(n) 1 0 (normal) 127 (curtailed) 2 0 (normal) 64 (level 1) 127 (level 2) 3 0 (normal) 42 (level 1) 84 (level 2) 127 (level 3) 4 Etc.

-   -   7. Model and predict the change in elastic load that should be         expected from the controlled, responsive asset system. The         output from this toolkit function into the overall algorithmic         responsibilities of the transactive node (e.g., the “toolkit         framework”) expects to receive a series of predicted changes in         electrical load ΔL_(n) for each IST interval n. The process or         model by which this prediction is made is somewhat unique for         the given asset system and its capabilities. The prediction will         be affected by the planned response level (as indicated by the         corresponding advisory control signal) and other information         used by the model as it makes its prediction.         -   The following models are expected to be relevant to this             toolkit function and are included by reference:             -   a. Electric tank water heater model. Toolkit function                 2.4_Residential Event-Driven Demand Response includes                 details about trends for electricity consumption by                 residential electric tank water heaters in the                 Northwest. There, one will find a lookup file that may                 be used to predict the average power that may be                 curtailed by time of day for week days and weekend days.                 The use of the lookup table should be identical for this                 function as for the referenced function. Please refer to             -   b. Thermostatic space conditioning dynamic model.                 Toolkit function 2.4_Residential Event-Driven Demand                 Response also documents a dynamic state model that may                 be used to predict the change in energy consumed by                 buildings based on predicted outdoor temperature, solar                 insolation, and parameters through which numbers and                 sizes of buildings, insulation levels, and other                 building qualities may be represented. The thermostat                 model tracks a representative building interior                 temperature that may, in turn, be affected by modeled                 occupancy set points and by demand-response levels.

Further Alternatives:

There might exist a preferable way to organize toolkit load functions according to (1) the way events are related to the TIS time series and (2) the asset system models. The present organization, in which these two elements have been combined into each toolkit function, is inefficient and uses multiple cross references and duplications.

The means by which TOU periods are specified from the TIS proved, while conceptually easy, to be relatively difficult to describe and specify. This function should be further refined as implementers learn ways to mathematically represent the process that has been described herein.

Subappendix A Revised High-Level Pseudo Code

While the pseudo code in the function's specification remains largely correct, the interpretation of selecting the event interval having the “maximum average TIS” was open to interpretation. If strictly followed, the algorithm would select only the events having minimum duration. The following general strategy proved useful.

The following general steps were

-   -   1. Parse future intervals into their local (not UTC) days. (This         cannot be strictly performed because long intervals, which were         aligned with UTC time, do not correctly align with midnight         local time.)     -   2. Review the history of the first day using TIS_(—)0 values         within the day. Use only the last of the historical relaxation         calculations within any 5-minute update interval and discard         other relaxation intervals that were overridden. Update the         numbers of time-of-use events, ongoing event duration, and total         event duration for the day.     -   3. Calculate average TIS for permutations of contiguous         intervals within the first and each remaining day that have an         allowed duration.     -   4. Select the permutation that gives the maximum average TIS.     -   5. Tentatively state that the new selected interval(s), plus any         prior-approved intervals, are part of the day's event(s).     -   6. Test the set of tentatively engaged intervals. If         -   a. Total event duration is not more than the maximum allowed             for day,         -   b. AND the number of events does not exceed the number             allowed for the day,         -   c. AND (the event duration is less than or equal to the             minimum OR the selected intervals' average TIS value is             greater than or equal to a threshold),         -   d. AND (the number of events fewer than or equal to the             minimum count OR the selected intervals' average TIS value             is greater than or equal to a threshold),         -   Then include tentative intervals among prior-selected             intervals.     -   7. Select the permutation that gives the next maximum average         TIS and go to repeat step 4.

6.3.14 Load Function Time-of-Use Distribution System Voltage Control (Function 3.5) Description

This toolkit load function is similar to Toolkit Function 2.2 Event-Driven Distribution System Voltage Control, except voltage is controlled in this function according to daily on- and off-peak time-of-use periods. (“Time-of-use,” as used here is more dynamic than time-of-use demand response is currently practiced. This function dynamically determines appropriate peak and off-peak periods based on the condition of a relatively dynamic incentive signal.) This toolkit function is applicable where voltage is to be controlled at two or more levels according to the value of the TIS and constraints input by utilities and where responses of the asset have been designed to occur according to daily on- and off-peak periods.

During the strategic design of toolkit load functions, it has been observed that the functions that share time-of-use objectives are very similar, and functions that control the same type of asset system are also similar. This present function makes efficient use of this observation and incorporates similar toolkit load function objectives and text by reference.

Block Input/Output Function Model: Inputs:

Include by reference the list of inputs in Toolkit Load Function 3.4 Residential Time-of-Use Demand Response.

-   -   CVRf—[dimensionless fraction]—ratio of relative percentage         change in energy that will accompany a relative percentage         change in voltage for a circuit or set of circuits. (Default         value=0.7) (The CRV factor depends on circuit type and circuit         characteristics, but it will often be unknown. A typical default         value has been provided based on readily available reports, but         a utility may use different and better numbers if they possess         better information about their circuits.) In principle, this         factor could be different for each feeder, but this formulation         will assume that only one factor has been defined for the         region. If multiple factors will be employed, the extension of         this toolkit function will be straightforward.     -   {ΔV₁, ΔV₂, . . . , ΔV_(l), ΔV_(L)}—[dimensionless         fraction]—fractional change in nominal system voltage enacted         under each response level l. These changes in voltage will         normally be negative, meaning the voltage will have been         decreased below its nominal set point.     -   {P₀, P₁, . . . , P_(n), . . . , P_(N−1)}[kW]—predicted average         nominal load during each IST_(n) interval for the entire region         in which the distribution voltage is being controlled. The         prediction is for the nominal condition No_(minal) which may be,         but is not necessarily, when ΔV is equal to zero. (What is         desirable here is to capture the percentage changes in voltage         that will occur during various transactive-control response         levels. It does not especially matter whether the nominal         voltage is already lowered at all times (“nominal”) for         conservation purposes.) It will be presumed that Toolkit Load         Function 1.0 Bulk Inelastic Load has been employed at this         transactive node to predict the load for this region that is         affected by distribution load control. Electrical load is         normally formulated with a negative sign.

Interim Calculation Products:

Same.

Outputs:

Same.

Pseudo Code Implementation:

-   -   1. Establish/update the statistical distribution of historical         TIS₀ values.     -   2. Update incentive thresholds for this system of assets.     -   3. Calculate averaged TIS values from the thresholds and         statistical information.     -   4. Determine TOU event periods for this system of assets.     -   5. Specify the prioritization of response levels.     -   6. Update the advisory control signal time series for this         system of assets.     -   7. Model and predict the change in elastic load that should be         expected from the controlled, responsive asset system. The         output from this toolkit function into the overall algorithmic         responsibilities of the transactive node (e.g., the “toolkit         framework”) expects to receive a series of predicted changes in         electrical load ΔL_(n) for each IST interval n.         -   The predicted change in electrical load ΔL_(n) for this             toolkit load function is strongly influenced by the             conservation voltage reduction factor CVRf. This factor is             usually known imprecisely, so one may rely upon a default             value.         -   The change in elastic load is zero until a TOU event occurs.             During IST intervals n*, during which a TOU response period             is planned for level l, the change in elastic load is             predicted by equation 1. (CRVf is normally calculated from             energy savings. Some might debate the way it has been             applied in equation 1 to individual intervals. The factor is             not perfectly applicable to short intervals, where immediate             changes in load might not be representative of long-term             changes in energy consumption. For this reason, the             prediction from equation 1 might be somewhat conservatively             made for very short intervals. The effect is probably             small.)

ΔL _(n*,l)=CVRf·ΔV _(l) ·P _(n*)  (1)

-   -   -   -   n*—[dimensionless index]—index of those IST_(n)                 intervals during which a TOU period is active at                 response level l.             -   ΔL_(n*,l)—[kW]—change in elastic load that has been                 induced by operating at response level l during IST                 interval n. ΔL_(n,l) is equal to zero for n≠n*. The sign                 of ΔL_(n*,l) should be positive where voltage has been                 reduced, thus reducing energy consumption and making                 more energy available to the region.             -   ΔV_(l)—[dimensionless fraction]—fractional change in                 system voltage enacted by the utility under response                 level l.             -   P_(n*)—[kW]—predicted average power that would have been                 consumed during this IST interval n* if no TOU event                 were planned in this region of the distribution circuit.

6.3.15 Load Function Time-of-Use Distribution System Voltage Control with Load Shedding Effect (Function 3.51) Description

This toolkit load function is based on Load Toolkit Function 3.5 Time-of-Use Distribution System Voltage Control, but includes the effect of concurrent shedding of customer loads (e.g. water heaters, thermostatic space conditioning, etc.) that use augmented conservation regulation. For example, this function should be used by Milton-Freewater's test case MF-02-1.2, in which time-of-use voltage reduction both earns conservation from circuit loads and triggers Grid Friendly™ water heaters to turn off.

This function relies on the approach that was formulated in toolkit functions 3.4 Residential Time-of-Use Demand Response and 3.5 Time-of-Use Distribution System Voltage Control.

Block Input/Output Function Model: Inputs:

-   -   Include by reference the list of inputs in Load Toolkit Function         3.4 Residential Time-of-Use Demand Response.     -   CVRf—[dimensionless number].     -   {ΔV₁, ΔV₂, . . . , ΔV_(l), . . . , ΔV_(L)}—[dimensionless         number].     -   {P₀, P₁, . . . , P_(n), . . . , P_(N−1)}—[kW].

Interim Calculation Products:

-   -   Same.

Outputs:

-   -   Same.

Pseudo Code Implementation:

-   -   1. Establish/update the statistical distribution of historical         TIS₀ values.     -   2. Update incentive thresholds for this system of assets.     -   3. Calculate averaged TIS values from the thresholds and         statistical information.     -   4. Determine TOU event periods for this system of assets.     -   5. Specify the prioritization of response levels.     -   6. Update the advisory control signal time series for this         Time-of-Use Distribution System Voltage Control asset system.     -   7. Model and predict the change in elastic load that should be         expected from the Time-of-Use Distribution System Voltage         Control asset system. The overall algorithmic framework of the         transactive node (the “toolkit framework”) expects to receive a         series of predicted changes in electrical load ΔL_(n) for each         IST interval n.         -   The predicted change in electrical load ΔL_(n) for this             toolkit load function is strongly influenced by the             conservation voltage reduction factor CVRf, which in turn is             dependent on the electrical characteristics of the system             and varies with the system load. This factor is usually             known imprecisely, so one rely upon a default value.         -   The change in elastic load is zero until a TOU event occurs.             During IST intervals n*, during which a TOU response period             is planned for level l, the change in elastic load is             predicted by equation 1. (CRVf is normally calculated from             energy savings. Some might debate the way it has been             applied in equation 1 to individual intervals. The factor is             not perfectly applicable to short intervals, where immediate             changes in load might not be representative of long-term             changes in energy consumption. For this reason, the             prediction from equation 1 might be somewhat conservatively             made for very short intervals. The effect is probably             small.) (Subtracting ΔL_(load) _(—) _(n*) from P_(n*)             changes the load-dependent CVRf factor. Given the limitation             of the project participants to precisely determine CVRf, the             interdependency between P_(n) and CVRf is disregarded in             equation 1.)

ΔL _(n*,l)=CVRf·ΔV _(l)·(P _(n*) −ΔL _(load) _(—) _(n*))+ΔL _(load) _(—) _(n)  (1)

-   -   -   -   n*—[dimensionless index]—index of those IST_(n)                 intervals during which a TOU period is active at                 response level l.             -   ΔL_(n*,l)—[kW]—change in elastic load that has been                 induced by reducing voltage at response level l during                 IST interval n. ΔL_(n,l) is equal to zero for n≠n*. The                 sign of ΔL_(n*,l) should be positive where voltage has                 been reduced, thus reducing energy consumption and                 making more energy available to the region.             -   ΔV_(l)—[dimensionless fraction]—fractional change in                 system voltage enacted by the utility under response                 level l.             -   P_(n*)—[kW]—predicted average power that would have been                 consumed during this IST interval n* if no voltage                 control or load shedding event were planned in this                 region of the distribution circuit.             -   ΔL_(load) _(—) _(n*)—[kW]—average change in power caused                 by the elastic behavior of customer loads in the region,                 where TOU voltage control is being applied, during IST                 interval n. ΔL_(load) _(—) _(n) is equal to zero for                 n≠n*. The sign of ΔL_(load) _(—) _(n*) should be                 positive where voltage has been reduced, and is computed                 as shown elsewhere in this application.

Example

-   -   CVRf=1 on average for a given utility feeder.     -   L=1 and ΔV₁=3%=0.03.     -   IST₀=midnight; TOU event scheduled to start at 7 a.m. (IST₃₃)         and end at 10 a.m. (IST₃₆).     -   P_(n) is known as shown in FIG. 1 below. P₃₃=9118 kW, P₃₄=9260         kW, and P₃₅=8812 kW.     -   During TOU event, 1000 water heaters will be triggered to turn         off. For example, load 33=1000×mean(0.817, 0.785, 0.738, 0.713)         kW=763 kW, ΔL_(load) _(—) ₃₄=1000×mean(0.716, 0.687, 0.672,         0.636) kW=678 kW, and ΔL_(load) _(—) ₃₅=1000×mean(0.615, 0.584,         0.563, 0.518) kW=570 kW.     -   Applying equation (1) above, ΔL₃₃=1×0.03×(9118−763)+763 kW=1014         kW, ΔL₃₄=1×0.03×(9260−678)+678 kW=935 kW, and         ΔL₃₅=1×0.03×(8812−570)+570 kW=817 kW.

FIG. 88 is an illustration 8800 of TOU voltage control concurrent with shedding water heaters.

6.3.16 Load Function Non-Renewal Generation Time-of-Use Demand Response (Function 3.7) Description

This function predicts the response from a non-renewable distributed generator demand-response system that will respond approximately daily to help mitigate peak conditions that are evident in an incentive signal. The peak period will be based on response constraints and the TIS incentive signal. (Note that this approach is more dynamic than typical time-of-use (TOU) demand response, in which daily peak and off-peak intervals remain immutable. The peak and off-peak periods recommended by this function may be assigned differently each day according to events that will have affected the predicted TIS incentive signals.) This function relies on the approach that was formulated in toolkit function 3.4 Residential Time-of-Use Demand Response.

A first objective of this function is to establish the time periods during which the response level(s) should occur, based upon the numbers and durations and preferred durations of these periods that are permitted for each response level. The daily events and their durations are positioned to best align with the levels of the TIS incentive signal that has been predicted for the day.

The function should then predict the change in load that will result from these events. Specifically, what additional energy will be generated at each prescribed response level.

Block Input/Output Function Model: Inputs:

-   -   m—[power per time: kW/minute]—maximum allowed linear rate of         change in generated power. This value may at times limit the         rate at which control changes are permitted and may thereby         modify the generation power predictions. This is a strictly         positive number. Default: 100 MW/minute (e.g., an essentially         infinite rate of change is allowed by default).     -   L—[dimensionless count]—Default: 1.     -   {Threshold₁, Threshold₂, . . . , Threshold_(l), . . . ,         Threshold_(L)}—[dimensionless fraction]. (Default={1/(L+1),         2/(L+1), . . . , l/(L+1), . . . , L/(L+1)})     -   {D_(min,week day,l), D_(min,weekend day,l),         D_(min,holiday,l)}—[time: minutes].     -   {N_(min,week day,l), N_(min,weekend day,l),         N_(min,holiday,l)}—[dimensionless count].     -   {N_(max,week day,l), N_(max,weekend day,l),         N_(max,holiday,l)}—[dimensionless count].     -   {D_(max,week day,l), D_(max,weekend day,l), D_(max,holiday,l),         D_(max event,l)}—[time duration: minutes].     -   {TIS₀(t), TIS₀(t−5), TIS₀(t−5k)}—[$/kWh].     -   {TIS₀, TIS₁, . . . , TIS_(K-1)}—[$/kWh].     -   {P_(weekday)(0), P_(weekday)(1), . . . , P_(weekday)(h),         P_(weekday)(23)}—[power: kW]—typical baseline power that is         generated during UTC hour h of a weekday day type by this         distributed generation resource. Additional inputs may be used         in implementations that anticipate more day types other than         weekdays and weekend days. Default: {0, 0, . . . }.     -   {P_(weekend)(0), P_(weekend)(1), . . . , P_(weekend)(h),         P_(weekend)(23)}—[kW]—typical baseline power that is generated         during hour h of a weekend day by this distributed generation         resource. Default: {0, 0, . . . }.     -   {ΔP₁, ΔP₂, . . . , ΔP_(L)}—[power: kW]—Change in generation that         may be anticipated at each of the L response levels, with         respect to inelastic load. It is presumed that the opportunity         to generate at each level may be assigned as a constant         regardless of hour of day. This list may be updated seasonally         for cogeneration plants that may be affected by changes in         seasonal thermal heating loads.

Interim Calculation Products:

-   -   {DIST(TIS_(0,min)), DIST(TIS_(0,min)+Δ$), . . . ,         DIST(TIS_(0,b)), . . . }—[dimensionless].     -   {φ(TIS_(0,1)), φ(TIS_(0,2)), . . . , φ(TIS_(0,b)), . . . ,         φ(TIS_(0,B))}—[dimensionless fraction].

Outputs:

-   -   {L₀, L₁, . . . , L_(K-1)}—[kW]—inelastic load (generation) from         these generators. The generated power that is predicted to occur         at response level 0 (e.g., no response) during each of the K IST         intervals. (This baseline series will normally become reported         as inelastic load. Caution should be used that its impact is not         double-counted. Also, it should be assumed that none of the         transitions during typical operations will be permitted to         exceed the allowed rate of change.)     -   {ACS₀, ACS₁, . . . , ACS_(K-1)}—[dimensionless].     -   {ΔL₀, ΔL₁, . . . , ΔL_(K-1)}—[kW].

Pseudo Code Implementation:

-   -   1. Establish/update the statistical distribution of historical         TIS₀ values.     -   2. Update incentive thresholds for this system of assets.     -   3. Calculate averaged TIS values from the thresholds and         statistical information.     -   4. Determine TOU event periods for this system of assets.     -   5. Specify the prioritization of response levels.     -   6. Update the advisory control signal time series for this         system of assets.     -   7. Model and predict the inelastic load and the change in         elastic load that should be expected from the controlled,         responsive asset system.         -   a. Inelastic Load—Case where no limit is imposed on rate of             change (essentially infinite rate of change)             -   The inelastic load (generation) from the distributed                 generator assets is the generated power that is expected                 to occur if the generators were unaffected by                 transactive control and operated normally, not in any                 response level. If there is no limit imposed on the rate                 that generation can occur, then the inelastic load is                 predicted simply from the hourly generation profiles for                 each day type, which are inputs to this toolkit                 function. Two day types—weekday and weekend (including                 holidays)—are defined, but the rule of equation 1 should                 be formulated for each day type that is being modeled                 for the given IST interval k.

$\begin{matrix} {L_{k} = \left\{ \begin{matrix} {{P_{weekday}\mspace{11mu} (h)\mspace{14mu} {or}\mspace{14mu} P_{weekend}\mspace{11mu} (h)},} & {{{if}\mspace{14mu}\left\lbrack {{IST}_{k},{IST}_{k + 1}} \right)} \subseteq \; \left\lbrack {{h\text{:}00.h} + {1\text{:}00}} \right)} \\ {{{mean}\; \left( {{P_{weekday}\left( h^{*} \right)},{P_{weekend}\; \left( h^{*} \right)}} \right)},} & {{{if}\mspace{14mu}\left\lbrack {{h^{*}\text{:}00},{h^{*} + {1\text{:}00}}} \right)} \Subset \mspace{11mu} \left\lbrack {{IST}_{k},{IST}_{k + 1}} \right)} \end{matrix} \right.} & (1) \end{matrix}$

-   -   -   -   -   L_(k)—[average power: kW]—inelastic load                     (generation) during interval [IST_(k),                     IST_(k+1))—Default; 0.00 kW.                 -   P_(weekday)(h)—[average power: kW]—typical weekday                     power generated by this resource during hour h.                 -   h—[hour]—UTC clock hour at which an hour-long                     interval starts. The notation h* has been used to                     refer to a set of hours that initiate hour-long                     intervals that are a subset of an IST interval. The                     hour-long interval starting at h has been shown as                     [h:00,h+1:00).

        -   b. Elastic Load—Case where no limit is imposed on rate of             change             -   Elastic load is the predicted change in generation when                 compared to the unaffected inelastic load prediction. In                 the case where no limit has been imposed on the rate of                 change in generation, the magnitudes of these changes                 are found by simply allocating the ΔP_(l) input values                 to the IST intervals having the corresponding response                 level l as shown in equation 2.

$\begin{matrix} {{\Delta \; L_{k}} = \left\{ \begin{matrix} {{\Delta \; P_{l}},} & {{{if}\mspace{14mu} {ACS}_{k}} = {ACS}_{l}} \\ {0,} & {otherwise} \end{matrix} \right.} & (2) \end{matrix}$

-   -   -   -   -   ΔL_(k)—[average power: kW]—change in power                     (generation)—the elastic load expected during the                     interval that begins at IST_(k).                 -   ΔP_(l)—[average power: kW]—the change in power                     (generation) expected at times that the modeled                     distributed generator resource is in its response                     level l.                 -   ACS_(k)—Advisory control signal that has been                     assigned by this function during the interval that                     starts at IST_(k).                 -   ACS_(l)—Advisory control signal that has been                     assigned if the modeled generator is to be at its                     response level l.

        -   c. Inelastic and Elastic Load where a limit has been imposed             on the rate of change of generated power. In the case where             the rate of change of generated electric power is to be             constrained, this function should keep track of the power             generated at the beginning of each interval. This attainable             power for the interval boundaries are at times modified by             the allowed rate of change m as shown in equation 3 and             FIG. 1. These are additional steps to be taken after             equations 1 and 2. The power at IST_(k+1) is predicted from             the power at IST_(k) and the allowed rate of change in             generated power m. (This formulation and equation 3 have a             minor challenge for the determination of p₀, the power at             time IST₀. This value should either be determined by current             measurement, or it should be inferred from the prior             calculation that was conducted during a prior update             interval. This implies a desire for the parameter to be             stored from one iteration to the next (e.g., the value p₁             from five minutes ago is now the best estimate of p₀; each             of these values refer to the same IST time value).)

$\begin{matrix} {p_{k + 1} = \left\{ \begin{matrix} {{\min \; \left( {{p_{k} + {m \cdot \left( {{IST}_{k + 1} - {IST}_{k}} \right)}},{L_{k} + {\Delta \; L_{k}^{\prime}}}} \right)},} & {{{{if}\mspace{14mu} L_{k}} + {\Delta \; L_{k}^{\prime}}} \geq p_{k}} \\ {{\max \; \left( {{p_{k} - {m \cdot \left( {{IST}_{k + 1} - {IST}_{k}} \right)}},{L_{k} + {\Delta \; L_{k}^{\prime}}}} \right)},} & {{{{if}\mspace{14mu} L_{k}} + {\Delta \; L_{k}^{\prime}}} < p_{k}} \end{matrix} \right.} & (3) \end{matrix}$

-   -   -   -   p_(k)—[power: kW]—generated power at beginning of                 interval IST_(k).             -   m—[power per time: kW/minute]—allowed rate of change in                 generated power. This formulation assumes the same                 restrictions apply for both ramping up and down.             -   L_(k)—[average interval power: kW]—inelastic load during                 interval IST_(k) as was calculated in equation 1 above.             -   ΔL′_(k)—[average interval power: kW]—elastic load during                 interval IST_(k) as was first calculated in equation 2                 above.

FIG. 89 is a series 8900 of plots that show possible scenarios for changes in generation during one interval. The average elastic load ΔL_(k) for the interval that starts at IST_(k) is then recalculated using the powers p_(k) and p_(k+1) at the intervals boundaries as shown in equation 4.

$\begin{matrix} {{\Delta \; L_{k}} = \left\{ \begin{matrix} {{\frac{{p_{k + 1} \cdot \left( {{IST}_{k + 1} - {IST}_{k} - \frac{p_{k + 1} - p_{k}}{m}} \right)} + \frac{p_{k + 1}^{2} - p_{k}^{2}}{2 \cdot m}}{{IST}_{k + 1} - {IST}_{k}} - L_{k}},} & {{{{if}\mspace{14mu} p_{k + 1}} \geq {p_{k}\mspace{14mu} {and}\mspace{14mu} p_{k + 1}}} = {L_{k} + {\Delta \; L_{k}^{\prime}}}} \\ {{\frac{{p_{k + 1} \cdot \left( {{IST}_{k + 1} - {IST}_{k} - \frac{p_{k} - p_{k + 1}}{m}} \right)} + \frac{p_{k}^{2} - p_{k + 1}^{2}}{2 \cdot m}}{{IST}_{k + 1} - {IST}_{k}} - L_{k}},} & {{{{if}\mspace{14mu} p_{k}} \geq {p_{k + 1}\mspace{14mu} {and}\mspace{14mu} p_{k + 1}}} = {L_{k} + {\Delta \; L_{k}^{\prime}}}} \\ {{\frac{p_{k} + p_{k + 1}}{2} - L_{k}},} & {otherwise} \end{matrix} \right.} & (4) \end{matrix}$

Steps 2-7 (and perhaps 1, too) should be iterated each update interval.

Further Alternatives:

-   -   1. This function has its event times invoked by relative TIS         values. For some future DG systems, there costs of operation         will be more completely modeled, and the boundaries between         time-of-use events will be based on absolute thresholds of the         TIS signal. Additional operational inputs like actual steam load         and price of fuel would be used by these future improvements to         this function.

6.3.17 Incentive Function General Infrastructure Cost (Function 4.0) Description

Where transactive control is applied at the device level, each device would have the opportunity to inject the impact of hardware costs (e.g., its infrastructure costs). However, where transactive control has been applied to large aggregate regions, the owner of the large aggregate transactive node may be unable or unwilling to accurately represent the impact of infrastructure costs on the delivered cost of energy. The purpose of this function, therefore, is to represent the influence of bulk infrastructure costs on the delivered cost of electrical energy where it might be impracticable to track the costs of individual infrastructure components.

The effect of this function should be to apply an offset to the calculation of the delivered cost of energy (e.g., the transactive incentive signal (TIS)). It is assumed by this function that the difference between the sum of existing resource costs and incentives, which are otherwise already represented in the TIS, and an accepted delivered cost of energy is attributable to infrastructure costs. (This assumption may be somewhat imperfect due to profit, labor costs, taxes and other impacts.)

This toolkit function may be applied at any of the transactive nodes, but it is desirable that transmission zone transactive nodes use this function to represent the bulk impact of generation and transmission infrastructure costs that might not have otherwise been included.

Negative C_(I) parameter outputs are to be disallowed in order to halt most occurrences of negative calculated TIS in the system.

Block Input/Output Function Model: Inputs:

TIS₀(n)—[cost/energy: default: $/kWh]—(series of real floating)—time series of the transactive control signal (TIS) at interval start time zero (IST₀) at each of a series of update intervals n. (The update interval may be 5 minutes. In certain embodiments, a TIS is calculated and transmitted at least once by this transactive node at each update interval. Of the interval values within each TIS, only the first, TIS₀, that refers to the nearest 5-minute interval is to be used by this function.) TIS—_(avg)—[cost/energy: default: $/kWh]—(real floating scalar)—typical, or long-term average, value of TIS₀(n). This value should be observed from or analyzed from calculated TIS values at this transactive node. This value is used only during initialization of the infrastructure cost parameter C_(I). The default value $0.04/kWh may be used, but doing so may introduce an initialization error that can take months to fully eliminate. TIS_(target)—[cost/energy: default: $/kWh]—(real floating scalar)—accepted reference baseline for what the long-term delivered average cost of energy (e.g., the TIS) should be at this transactive node. In some cases, acceptable target TIS values have been found among energy supply costs in utilities' annual reports. Alternatively, the lowest customer costs that a utility passes along to its customers, too, might be an acceptable surrogate for the target TIS. Default: $0.05/kWh. ΣP_(G)—[power: default: kW]—(real floating scalar)—long-term average of the sum of power imported into and generated within the boundaries of this transactive node. This parameter is a long-term average of the denominator of the Update TIS framework function. This parameter is mostly static, but it may be updated quarterly or yearly by the owner of the transactive node. This parameter affects that rate at which the function's proportional controller tracks the infrastructure cost parameter C_(I). The accuracy of this parameter is not critical. The default value 1 GW should be used only as a last resort for this parameter. This default value will virtually disable this function for most transactive nodes so that the infrastructure cost will not be tracked. α—[dimensionless]—(real floating scalar)—factor used in the proportional controller to affect the rate at which the infrastructure cost parameter should track the TIS. Default value: 0.0001, assuming that updates occur every 5 minutes.

Outputs:

C_(I)—[cost/time: default units: $/h]—Parameter defined and used in Transactive Node and Toolkit Functions and Transactive Control System Data Collection. Time series of cost per time duration to be applied at defined future time intervals. In this function, this output is a correction that approximates the amortized costs of infrastructure over time. A remedial action was initiated to disallow this output parameter from becoming negative.

Pseudo Code Implementation:

-   -   (1) Initialize the infrastructure cost parameter C_(I) for this         transactive node. Because this function relies on an extremely         slow, low-pass feedback loop, it is strongly recommended that         the function's infrastructure cost parameter C_(I) be         initialized to a reasonable value. If this step is not         performed, the function will eventually identify an acceptable         offset that represents infrastructure costs, but it will slowly         and asymptotically approach the offset over multiple months. The         formulation and details of this initialization may be found in         SubAppendix A.         -   Assign the initial value to the infrastructure cost             parameter as shown in Equation 1.

C _(I)=(TIS_(target)−TIS_(avg))·ΣP _(G)  (1)

-   -   (2) Replicate the initialized or updated infrastructure cost         parameter into the elements of the series of values expected by         the toolkit framework and publish the new series to the toolkit         framework.         -   For k=0 to K, where K=56 for certain embodiments.             -   Set C_(I)(k)=C_(I)             -   Next k             -   Publish {C_(I)(0), C_(I)(1), . . . , C_(I)(K)} to this                 transactive node's toolkit framework for this function.     -   (3) After an update interval (e.g. every 5 minutes), update the         calculated infrastructure cost based on the target TIS, actual         recent TIS₀, typical sum of imported and generated power, and         parameter α. Equation (2) can be modified to disallow negative         C_(I) output parameters.

C _(I,n)=maximum(0,C _(I,n-1)+α·(TIS_(target)−TIS_(0,n-1))·ΣP _(G))  (2)

-   -   (4) Loop back to step (2).

Subappendix A Details about Initializing and Updating Infrastructure Cost Parameter C_(I)

This appendix takes one through formulations on which the initialization and updating of the infrastructure cost parameter C_(I) output of this function is based.

Refer to the framework function by which the TIS for an interval is calculated at a transactive node, copied here as Equation A1. The numerator is a total cost, and the denominator is the sum of electrical energy that is imported into or generated within this transactive node during interval n. The resulting division yields a unit cost of energy, the TIS, which represents the delivered cost of energy at this location in the system.

$\begin{matrix} {{TIS}_{n} = \frac{\begin{matrix} {{\sum\limits_{a = 1}^{A}\; {{C_{E,a,n} \cdot {\hat{P}}_{G,a,n} \cdot \Delta}\; t_{n}}} +} \\ {{\sum\limits_{b = 1}^{B}\; {C_{C,b,n} \cdot {\hat{P}}_{C,b,n}}} + {\sum\limits_{c = 1}^{C}\; {{C_{I,c,{.n}} \cdot \Delta}\; t_{n}}} + {\sum\limits_{d = 1}^{D}\; C_{O,d,n}}} \end{matrix}}{\sum\limits_{a = 1}^{A}\; {{{\hat{P}}_{G,a,n} \cdot \Delta}\; t_{n}}}} & ({A1}) \end{matrix}$

We assume that the costs in the numerator prior to applying this function can be lumped together as shown in Equation A2. These costs will neither affect nor be affected by this formulation.

$\begin{matrix} {{TIS}_{n}^{old} = \frac{{Cost}_{n}^{old}}{\sum\limits_{a = 1}^{A}\; {{{\hat{P}}_{G,a,n} \cdot \Delta}\; t_{n}}}} & ({A2}) \end{matrix}$

A term is added to both sides of Equation A2 to represent an infrastructure cost offset that had not been represented in the prior formulation. See Equation A3. The new TIS_(target) may be thought of a corrected version of the TIS and may be independently assigned based on long-term-average energy supply costs or other representations of the delivered cost of energy at this system location. An infrastructure term C_(I) was selected for this function because the new infrastructure costs will be modeled as being amortized evenly over time.

$\begin{matrix} {{TIS}_{target} = {{{TIS}_{n}^{old} + {{Infrastructure}\mspace{14mu} {Cost}\mspace{14mu} {Offset}}} = \frac{{Cost}_{n}^{old} + {{C_{I} \cdot \Delta}\; t}}{\sum\limits_{a = 1}^{A}\; {{{\hat{P}}_{G,a,n} \cdot \Delta}\; t_{n}}}}} & ({A3}) \end{matrix}$

Equation A4 is found by subtracting Equation A2 from Equation A3. Equation A4 states a relationship between the independent reference TIS_(target), calculated TIS values, the new infrastructure cost parameter C_(I), and the sum of imported and generated power at this transactive node.

$\begin{matrix} {{{TIS}_{target} - {TIS}_{n}^{old}} = {{{Infrastructure}\mspace{14mu} {Cost}\mspace{14mu} {Offset}} = \frac{C_{I}}{\sum\limits_{a = 1}^{A}\; {\hat{P}}_{G,a,n}}}} & ({A4}) \end{matrix}$

We rearrange Equation A4 to solve for the new infrastructure cost parameter, as shown in Equation A5.

$\begin{matrix} {C_{I} = {\left( {{TIS}_{target} - {TIS}_{n}^{old}} \right) \cdot {\sum\limits_{a = 1}^{A}\; {\hat{P}}_{G,a,n}}}} & ({A5}) \end{matrix}$

Equation A5 gives us insights about how to initialize the infrastructure cost parameter: Because the infrastructure cost parameter and target TIS are relatively constant, they should be compared to long-term averaged representations of the old TIS and sum of imported and generated power. Ideally, this node would be allowed to operate for months before this function is implemented so that these “typical” values could be learned. Realistically, one may have little or no prior TIS and power values to average. Some off-line analysis can be performed. Regardless, any errors during initialization will eventually be erased by the operation of the function's proportional controller.

$\begin{matrix} {C_{I} = {\left( {{TIS}_{target} - {TIS}_{avg}^{old}} \right) \cdot {\sum\limits_{a = 1}^{A}\; {\hat{P}}_{G,a,{avg}}}}} & ({A6}) \end{matrix}$

Equation A5 is also the basis for the formulation of a proportional controller by which the estimated value of C_(I) may be gradually improved. Equation A7 suggests how C_(I) may be updated from a prior version of itself and an approximation of the value from Equation A5. This is also illustrated in diagram 9000 of FIG. 90. The new parameter α determines the weight of the proportional controller and, therefore, the rate of convergence. If the time between update intervals n−1 and n is 5 minutes, then setting α=0.0001 will ensure a response time near a month, which is about right for the tracking of infrastructure costs. Because C_(I) varies extremely slowly, even longer update intervals (and correspondingly revised α) may be selected by implementers of this function.

Equation A7 can be modified to disallow negative C_(I) output parameter.

$\begin{matrix} {C_{I,n} = {{maxium}\left( {0,{C_{I,{n - 1}} + {\alpha \cdot \left( {{TIS}_{target} - {TIS}_{n - 1}^{old}} \right) \cdot {\sum\limits_{a = 1}^{A}\; {\hat{P}}_{G,a,{avg}}}}}} \right)}} & ({A7}) \end{matrix}$

It is presumed that recent calculations of the TIS (e.g., TIS_(n−1)) will be available to this function at this node. However, it is recommended that the constant, “typical,” value for the sum of imported and generated power should be used because access to this sum may not be readily available and is not warranted by the precision used by this function.

FIG. 90 is a diagram 9000 illustrating an infrastructure cost control diagram

FIG. 91 and FIG. 92 show examples of how the infrastructure cost estimate and TIS improve over time for different α parameter values using the iterative approach of Equation A7 at 5-minute intervals. In particular, FIG. 91 shows a graph 9110 illustrating the improvement of uninitialized infrastructure cost estimate for different α parameter selections assuming 5-minute update intervals. FIG. 92 shows a graph 9120 illustrating the uninitialized correction of TIS over time for different α parameter selections assuming 5-minute update intervals. In this instance, the TIS was 80% of the Target TIS at the time this function is activated.

6.3.18 Load Function Battery Storage—Real-Time (Function 4.1) Description

This function is applicable to battery storage systems that respond very dynamically to the TIS and other local conditions and provide also a continuum of charge and discharge levels. (If the battery system has only a few levels of response available to it (e.g. full charge, full discharge, and inactive) then the implementer should select a time-of-use function to model the battery system's behavior.) The function will recommend the appropriate charge and discharge rate based on the system's power capacity, state-of-charge, and historical and predicted incentive signals. The implementer is able to limit the responsiveness of his system using additional preferences.

All of the load or generation by a battery system is considered elastic; none is inelastic. An assumption is made in this present formulation that battery system inefficiency (e.g., losses and auxiliary loads) may be ignored.

Block Input/Output Function Model: Inputs:

-   -   {IST₀, IST₁, . . . , IST_(n), . . . , IST_(N)}—[UTC         time]—current interval start time (IST) time series.     -   {TIS₀, TIS₁, . . . , TIS_(n), . . . ,         TIS_(N−1)}—[$/kWh]—transactive incentive signal (TIS) time         series. Time series of predicted incentives.     -   SOC⁻¹—[kWh]—present state of battery charge just prior to the         prediction intervals of the current IST time series. This is the         known starting point from which battery charge should be         managed.     -   SOC_(max)—[kWh]—maximum state of charge that will be allowed for         this battery. This function will assume this constraint is         constant over time.     -   SOC_(min)—[kWh]—minimum state of charge that will be allowed for         this battery. This function will assume this constraint is         constant over time.     -   E—[kWh]—total battery energy capacity.     -   P_(c)—[kW]—nameplate rating for the rate at which the battery         system may be charged. This function will assume this constraint         is constant over time.     -   P_(d)—[kW]—nameplate rating for the rate at which the battery         system may be discharged. This function will assume this         constraint is constant over time.

Interim Calculation Products:

-   -   {Δt₀, Δt₁, . . . , Δt_(n), . . . , Δt_(N−1)}—[time:         minutes]—duration of each IST interval in the current IST time         series.     -   {SOC₀, SOC₁, . . . , SOC_(n), . . . , SOC_(N−1)}—[%]—predicted         state of battery charge at the end of each IST interval using         the predicted charge and discharge profile.

Outputs:

-   -   {ΔL₁, ΔL₂, . . . , ΔL_(n), . . . , ΔL_(N−1)}—[kW]—predicted         change in elastic load for each IST interval.     -   {S₁, S₂, . . . , S_(n), . . . ,         S_(N−1)}—[dimensionless]—advisory output signal to the battery         system.

Pseudo Code Implementation:

-   -   1. Predict the power to be consumed or generated during each         current IST interval (e.g., its elastic load prediction).         -   Define state relationships for the battery system as in             equations 1 through 5. The batteries' state of charge at the             end of intervals n are its states x.

$\begin{matrix} {x = {\begin{bmatrix} x_{0} \\ x_{1} \\ \ldots \\ x_{N - 1} \end{bmatrix} \equiv \begin{bmatrix} {SOC}_{0} \\ {SOC}_{1} \\ \ldots \\ {SOC}_{N - 1} \end{bmatrix}}} & (1) \end{matrix}$

-   -   -   The change in state Δx is equivalent to the rate of battery             charge or discharge during the corresponding interval, which             incidentally is also the change in elastic load ΔL for the             interval. (If the change in state of charge Δx is negative,             this means that the battery system should have discharged             some of its energy during the interval. The corresponding             change in load ΔL should reduce the apparent load at this             location much as would happen if load were curtailed.             Ultimately, the correctness of the sign convention will             depend on how the outputs of this function are to be used.             If load is a generally a positive quantity, then charging of             a battery is a positive load, and discharging is a negative             load. This discussion contradicts the sign convention shown             in Equation 2, in which a negative load sign convention is             used.) The change in elastic load ΔL is an important output             from this function that is expected by the Toolkit             Framework.

$\begin{matrix} {{\Delta \; x} = {\begin{bmatrix} {\Delta \; x_{0}} \\ {\Delta \; x_{2}} \\ \ldots \\ {\Delta \; x_{N - 1}} \end{bmatrix} \equiv \begin{bmatrix} {{- \Delta}\; L_{0}} \\ {{- \Delta}\; L_{1}} \\ \ldots \\ {{- \Delta}\; L_{N - 1}} \end{bmatrix}}} & (2) \end{matrix}$

-   -   -   Difference equation 3 is the state relationship to which             this physical system should adhere.

Δx=A·x+b,  (3)

-   -   -   where

$\begin{matrix} {{A \equiv \begin{bmatrix} \frac{1}{\Delta \; t_{0}} & 0 & \ldots & 0 \\ \frac{- 1}{\Delta \; t_{1}} & \frac{1}{\Delta \; t_{1}} & \ldots & 0 \\ \ldots & \ldots & \ldots & \ldots \\ 0 & \ldots & {- \frac{1}{\Delta \; t_{N - 1}}} & \frac{1}{\Delta \; t_{N - 1}} \end{bmatrix}}{and}} & (4) \\ {b \equiv {\begin{bmatrix} \frac{- {SOC}_{- 1}}{\Delta \; t_{0}} \\ 0 \\ \ldots \\ 0 \end{bmatrix}.}} & (5) \end{matrix}$

-   -   -   One important constraint is that the rate of charge or             discharge in each interval n should be bounded by the             physical capabilities of the conversion equipment. The             bounds are the physical nameplate ratings of the conversion             equipment.

P _(c) ≦Δx _(n) ≦P _(d)  (6)

-   -   -   The state of charge itself is often constrained at each             interval to lie within prescribed boundaries. Only a             fraction of a battery system's total energy capacity may be             available to use.

SOC_(min) ≦x _(n)≦SOC_(max)  (7)

-   -   -   The augmented cost function is the sum of incentive costs             received (and paid) at times that the battery system is             being charged or discharged. One strives to maximize this             cost function. Doing so would mean that the battery system             is doing its best to charge while incentives are low and             discharge while incentives are high in a way that will             maximize its overall incentive.

J=ƒ ₀(x,Δt,TIS)+ƒ₁(x)+ƒ₂(x),  (8)

-   -   -   where f₀ is the main economic incentive to be maximized over             the duration that a TIS signal has been defined.

$\begin{matrix} {f_{0} = {{- {\sum\limits_{n = 0}^{N - 1}\; {\Delta \; {x_{n} \cdot {TIS}_{n} \cdot \Delta}\; t_{n}}}} = {- {\sum\limits_{n = 0}^{N - 1}\; {\sum\limits_{m = 0}^{M - 1}\; {{\left( {{a_{n,m} \cdot x_{m}} + b_{n}} \right) \cdot {TIS}_{n} \cdot \Delta}\; {t_{n}.}}}}}}} & (9) \end{matrix}$

-   -   -   The constraints on state of charge may be incorporated via             penalty function f₁, thus avoiding the use of additional             Lagrangian terms and allowing a more direct solution             approach. This penalty function creates more accurate             solutions for successive integers k=1, 2, . . . .

$\begin{matrix} {f_{1} = {- {\sum\limits_{n = 0}^{N - 1}\; \left( \frac{\left( {x_{n} - {SOC}_{\max}} \right) + \left( {x_{n} - {SOC}_{\min}} \right)}{{SOC}_{\max} - {SOC}_{\min}} \right)^{2k}}}} & (10) \end{matrix}$

-   -   -   Similarly, the constraints on the rates of charge or             discharge may be imposed by penalty function f₂ as is shown             in equation 11. Again, this penalty function will enforce a             more accurate solution for successive integers k=1, 2, . . .             .

$\begin{matrix} {f_{2} = {{- {\sum\limits_{n = 0}^{N - 1}\; \left( \frac{\left( {{\Delta \; x_{n}} - P_{c}} \right) + \left( {{\Delta \; x_{n}} - P_{d}} \right)}{P_{c} - P_{d}} \right)^{2k}}} = {- {\sum\limits_{n = 0}^{N - 1}\; {\sum\limits_{m = 0}^{M - 1}\; \left( \frac{{2 \cdot \left( {{a_{n,m} \cdot x_{m}} + b_{n}} \right)} - P_{c} - P_{d}}{P_{c} - P_{d}} \right)^{2k}}}}}} & (11) \end{matrix}$

-   -   -   Now that the augmented cost function J has been entirely             stated in respect to the states x, one may use the necessary             condition of equation 12 to solve for state of charge x_(n)             at the end of each interval n.

$\begin{matrix} {{\frac{\partial J}{\partial x_{n}} = 0},{{{for}\mspace{14mu} n} - 0},1,2,\ldots \mspace{11mu},{N - 1}} & (12) \end{matrix}$

-   -   -   In turn, predicted charge rate may also be calculated from             equation 3 resulting in the important predicted elastic             change in load at each interval ΔL_(n).         -   Using constraint integer k=1, equation 12 give us N             equations which may be solved for x_(n) in respect to             x_(n−1) and x_(n+1) as shown in equation 13. (The terms of b             vector have been omitted from this formulation. This general             equation 13 is set up for solution by either relaxation or             by matrix inversion, where starting and ending states are             assumed to be known. Hammerstrom has solved these equations             in MS Excel using relaxation and iterations. The solution is             somewhat soft, allowing minor violations of the stated             constraints to persist. These constraint violations could be             reduced by using larger k values or by using altogether             other, sharper penalty functions. It is easiest to assert             the final value x_(N) in this formulation. A proper             optimization would set the final state at SOC_(min),             however, which is unrealistic and undesirable. A preferred             method is to set SOC_(N) equal to starting initial value             SOC-₁, in which case the relaxation solution is fully             specified.)

$\begin{matrix} {0 = {\frac{\partial J}{\partial x_{n}} = {{- \left( {{{a_{n,n} \cdot {TIS}_{n} \cdot \Delta}\; t_{n}} + {{a_{{n + 1},n} \cdot {TIS}_{n + 1} \cdot \Delta}\; t_{n + 1}}} \right)} - {\frac{8}{\left( {{SOC}_{\max} - {SOC}_{\min}} \right)^{2}} \cdot x_{n}} + \frac{4 \cdot \left( {{SOC}_{\max} + {SOC}_{\min}} \right)}{\left( {{SOC}_{\max} - {SOC}_{\min}} \right)^{2}} - {\frac{8 \cdot \left( {a_{n,n}^{2} + a_{{n + 1},n}^{2}} \right)}{\left( {P_{c} - P_{d}} \right)^{2}} \cdot x_{n}} - {\frac{8 \cdot a_{n,{n - 1}} \cdot a_{n,n}}{\left( {P_{c} - P_{d}} \right)^{2}} \cdot x_{n - 1}} - {\frac{8 \cdot a_{{n + 1},{n + 1}} \cdot a_{{n + 1},n}}{\left( {P_{c} - P_{d}} \right)^{2}} \cdot x_{n + 1}} + \frac{{4 \cdot \left( {P_{d} + P_{c}} \right)}\left( {a_{n,n} + a_{{n + 1},n}} \right)}{\left( {P_{c} - P_{d}} \right)^{2}}}}} & (13) \end{matrix}$

-   -   2. Generate the advisory signal time series prediction for the         battery system. After the desired charge rate Δx vector has been         solved, the charge rates should be stated as changes in elastic         load ΔL_(n) at each interval n using the relationship of         equation 2. One should also state an advisory control signal S         that will be sent to the battery system. The advisory control         signal has been specified as an integer and may be calculated as         the closest integer in the assignment shown in equation 14.

$\begin{matrix} {S_{n} \equiv \left\{ \begin{matrix} {{{{- 127} \cdot \Delta}\; x_{n}},} & {{\Delta \; x_{n}} \geq 0} \\ {{{- 127} \cdot \frac{\Delta \; x_{n}}{P_{d}}},} & {{\Delta \; x_{n}} < 0} \end{matrix} \right.} & (14) \end{matrix}$

Further Alternatives:

-   -   1. Additional steps could probably be taken to make the         application of this strategy more formulaic for a specific         implementer.     -   2. A completed example would probably be useful in an appendix         to this toolkit function.     -   3. Sharper penalty functions may be used to make the solution         more accurate and which would permit fewer soft constraint         violations.     -   4. The formulation should probably be normalized. The weights of         the functions f₀, f₁, and f₂ do not enforce a true economic         optimization when the impact of f₀ may be less at times than         that of the constraint functions.     -   5. While the constraints on state of charge and charge rate have         been stated as constants, these constraints may, in fact, be         functions of time and should be thought of as an allowed         operational envelope. Letting these envelopes be more dynamic         does not break this formulation, but it does lead to the         formulation being tweaked to state constraints as functions of         time intervals n.     -   6. Implementation may wish to specify a dead zone which has not         yet been accommodated in this formulation.

6.3.19 Incentive Function Transmission Flowgate (Function 5.1) Description

This function is to predict the MW flow and the cost of a transmission flowgate for each interval start times {IST_(n)} (e.g., n=0, 1, . . . , 56) used by the toolkit framework. A transmission flowgate is potentially congested transmission corridor defined between two transmission zones. A flowgate may consists of one of more than more transmission devices, such as high voltage AC/DC overhead lines and/or transformers.

With a given network topology, generation shift factors (SF) to a specific flowgate can be calculated by a network analysis application. Flowgates are modeled as linear inequality constraints using these shift factors in the Economic Dispatch (ED) Linear Programming problem. When a flowgate constraint is binding at its reliability flow limit, generators can be be redispatched “out-of-merit” according to their shift factors to the flowgate, in order to relieve the congestion. Such redispatch will lead to non-zero operational cost to a binding flowgate (aka shadow price of a constraint in Linear Programming). The physical meaning of the cost of a flowgate is the cost saving with one addition MW added to the limit of flowgate, which will increase one MW generation (cheaper) from the sending end of the flowgate and decrease one MW generation (more expensive) from the receiving end of the flowgate.

FIG. 93 is a diagram 9330 of an exemplary block input/output function model, which is discussed below.

Inputs:

-   -   Predicted price of fuel, which may be either constant or a         dynamic time series, depending on the fuel.     -   Representative amortized infrastructure cost. (In some cases,         the infrastructure costs will be stated as functions of many         variables, including local costs of money, taxes, regulations,         etc.)     -   Planned generator schedule(s), such as Federal hydro schedules.     -   Constant heat rate curves of fossil generators.     -   BPA Load Forecast.     -   Historical BPA Netmom savecases, which are used to produce         generation and load profiles for any given hour of a day in a         week of a specific season.     -   WECC Flowgate definition

Outputs:

-   -   Predicted flowgate flow P_(FG,IST) For a Transmission Flowgate         FG for time series using the intervals of the current IST time         series.     -   Corresponding predicted costs of each binding Flowgate         C_(FG,IST) using the intervals of the current IST time series.         If a flowgate is not congested (non-binding) in a particular         interval, its cost will be zero.

Pseudo Code Implementation:

-   -   1. Process inputs from BPA;     -   2. Complement input data with the model data from historical         Netmom savecases and WECC heat rate curves;     -   3. Solve a multi-interval economic dispatch problem which         produces MW flow P_(FG,t) and shadow price based cost C_(FG,t)         for each flowgate FG at each scheduling interval t     -   4. Calculate P_(FG/IST) for transmission Flowgate FG and         interval IST;         -   P_(FG,IST)=P_(FG,t), where t is covers the majority portion             of an IST interval     -   5. Compute the operational cost C_(FG/IST) for each transmission         flowgate FG and each IST interval;         -   C_(FG,IST)=C_(FG,t)/T_(t)*T_(IST), where t is covers the             majority portion of an IST interval

6.3.20 Incentive Function Equipment and Line Constraints (Function 5.2) Description

Discourage consumption of energy downstream from constrained distribution equipment, including distribution lines. Applies to transactive nodes that are in a position to mitigate their constraints by increasing the delivered cost of energy to downstream transactive nodes. Intended to be used where constraints may be correlated to specific equipment. Does not apply to transmission flowgates.

Block Input/Output Function Model: Inputs:

Predicted capacity to which this function applies. Function which estimates the cost impacts of exceeding the capacity constraint.

Outputs:

Predicted capacity cost time series C_(c) and corresponding capacity time series P_(c).

6.3.21 Incentive Function BPA Demand Charges (Function 7.1) Description

This function predicts the impact of demand charges that the Bonneville Power Administration (BPA) will apply to its customer utilities according to interpretation of its intricate Tiered Rate Methodology (TRM). The TRM explains how BPA's demand charges are to be allocated to its customer utilities at the conclusion of each month. However, since the transactive control and coordination system is predictive, the demand charge impacts of the methodology should be predicted instead. This function can, at best, estimate the demand charge impacts from the TRM.

Many components of the TRM duplicate energy costs that will already be represented in the transactive incentive signal (TIS) by electrically upstream locations. Generally, transactive control applies energy costs at the points where electrical energy is generated and fed into the electrical power grid. These influences should not be duplicated or double-counted. Therefore, this function should only insert the unique demand charge impacts from the TRM that apply specifically to the utilities. This may be achieved by applying upward pressure to the TIS—by adding, to the TIS computation, the product of the pair of capacity cost C_(C) and average power capacity P_(C)—during and around a time interval when the transactive feedback signal (TFS) predicts the occurrence of a peak that exceeds the highest peak that has already been recorded during the time elapsed for the calendar month prior to the start time (e.g., IST₀) of the TFS. If the increased TIS, in turn, applies enough downward pressure on the TFS, the predicted peak may be lowered enough to prevent any additional demand charge.

Normally, an electric utility would be the entity to apply this function. This function applies to a “utility” transactive node or to the transactive node that represents the perspective of an electric utility.

Block Input/Output Function Model: Inputs:

-   -   HLH—[number of hours]—Heavy Load Hour for every month of the         year. The daily HLH periods are defined by the North American         Electric Reliability Corporation (NERC), but should be updated         yearly or whenever there is a change.     -   C_(demand)—[$/kW]—BPA demand rate for every month for two years,         as approved by Federal Energy Regulatory Commission (FERC) and         published by BPA at the beginning of every other fiscal year         (starting in October). This is to be updated every other year in         this function or whenever there is a change.     -   CDQ—[kW]—Contract Demand Quantity for every month of the year,         as computed yearly by BPA for each of the customer/utility. This         is to be updated yearly in this function or whenever there is a         change.     -   W_(T1-HLH,m0)—[kWh]—planned Tier 1 HLH energy for the present         month m when this function is employed for the very first time;         this is an initial condition.     -   W_(T1-HLH m+1)—[kWh]—planned Tier 1 HLH energy for the upcoming         month m+1. Since the transactive control and coordination system         provides predictions for a 4-day horizon, this should be made         available to this function at least 4 days prior to the start of         an upcoming month.     -   P_(nonFed) _(—) _(fix) (optional)—[kW]—monthly fixed non-Federal         power capacity that is planned by or contracted to a customer.         This may include Tier 2 power capacity, Secondary Credit         Service, Super Peak Credit, and any other energy resources that         qualify as fixed non-Federal. This is to be updated whenever         there is a change in the planned or contracted power capacity.     -   P_(nonFed) _(—) _(var) (optional)—[kW]—monthly variable         non-Federal power capacity that is available to a customer. An         example is small-scale renewables resources, which may be         considered as negative loads. Due to the variability of such         resources, their power outputs will have to be predicted, for         each of IST intervals, to be used in this function. P_(nonFed)         _(—) _(var) may be set to zero if it is small 5%) compared to         P_(nonFed) _(—) _(fix).     -   TFS_(n)—[kW]—Transactive Feedback Signal, which is available         within the transactive node framework, where, for example, n=0,         1, . . . , 55. The TFS is only to be used when it represents the         total load for the customer's service territory. If that is not         the case, the customer should use a secondary source for the         prediction of its total utility load.     -   IST_(n)—Present time series interval start times used by the         toolkit framework, where, for example, n=0, 1, . . . , 56. (In         some embodiments, there is no prediction to correspond with         IST_(n) for n=56. This last IST is simply used to make it clear         when the final interval concludes.)     -   K—dimensionless—scaling factor (a constant) by which the effect         of the demand charge on the TIS may be scaled. This is to be set         to 1.0 until it becomes clear that it will be used.

Interim Calculation Products:

-   -   aHLH—[kW]—average monthly Tier 1 load served during the HLH of         the month.     -   P_(SCS-HLH) (optional)—[kW]—average monthly SCS load served         during the HLH of the month.     -   P_(th)—[kW]—threshold power capacity above which demand charge         may be incurred.     -   P′_(demand)—[kW]—Demand amount/capacity at a given point in a         month. This is updated every time a higher demand capacity is         recorded. At the end of the month, this should be the same as         the usual demand amount—also defined as the Demand Charge         Billing Determinant—which is used to compute the customer's         demand charge for the month.     -   A—[kWh]—average energy above P_(th) during an interval where and         surrounding where P_(C) (defined below) exceeds CSP′.

Outputs:

-   -   P_(C,n)—[kW]—average power capacity, corresponding to IST_(n).     -   C_(C,n)—[$/kW]—capacity costs, corresponding to IST_(n).

Pseudo Code Implementation:

-   -   1. Convert power capacities in units kW, if necessary.     -   2. Convert energy values in units kWh, if necessary.     -   3. Convert demand rates in units $/kW, if necessary.         -   Denote present month by m and upcoming month by m+1.     -   4. Initializations:

P′ _(demand,m)=0  (1)

P′ _(demand,m+1)=0  (2)

W _(T1-HLH,m) =W _(T1-HLH,m0)  (3)

-   -   5. Computations:

$\begin{matrix} {\mspace{79mu} {{a\; H\; L\; H_{m}} = \frac{W_{{{T\; 1} - {HLH}},m}}{H\; L\; H_{m}}}} & (4) \\ {\mspace{79mu} {{a\; H\; L\; H_{m + 1}} = \frac{W_{{{T\; 1} - {HLH}},{m + 1}}}{H\; L\; G_{m + 1}}}} & (5) \\ {\mspace{79mu} {P_{{th},m,n} = {{a\; H\; L\; H_{m}} + {C\; D\; Q_{m}} + P_{{nonFed\_ fix},m} + P_{{nonFed\_ var},n}}}} & (6) \\ {P_{{th},{m + 1},n} = {{a\; H\; L\; H_{m + 1}} + {C\; D\; Q_{m + 1}} + P_{{nonFed\_ fix},{m + 1}} + P_{{nonFed\_ var},{m + 1},n}}} & (7) \\ {\mspace{79mu} {{for}{{n = 0},1,\ldots \mspace{14mu},{{55\text{:}\mspace{14mu} P_{C,n}} = \left\{ \begin{matrix} {{\max \left( {0,{{T\; F\; S_{n}} - P_{{th},m,n}}} \right)},} & {{{if}\mspace{14mu} n} \in m} \\ {{\max \left( {0,{{T\; F\; S_{n}} - P_{{th},{m + 1},n}}} \right)},} & {{{if}\mspace{14mu} n} \in {m + 1}} \end{matrix} \right.}}}} & (8) \\ {\mspace{79mu} {{for}\mspace{79mu} {{n \in {m\text{:}\mspace{14mu} n_{{demand},m}}} = \left\{ \begin{matrix} {n,} & {{{if}\mspace{14mu} {\max \left( P_{C,n} \right)}} > P_{{demand},m}} \\ {Ø,} & {{otherwise},} \end{matrix} \right.}}} & (9) \end{matrix}$

-   -   -   where Ø represents the null set.

$\begin{matrix} {\mspace{79mu} {{for}\mspace{79mu} {n \in {m + {1\text{:}\mspace{14mu} n_{{demand},{m + 1}}} + \left\{ \begin{matrix} {n,} & {{{if}\mspace{14mu} {\max \left( P_{C,n} \right)}} > P_{{demand},{m + 1}}} \\ {Ø,} & {otherwise} \end{matrix} \right.}}}} & (10) \\ {\mspace{79mu} {{if}{{n_{{demand},m} \neq {Ø\text{:}\mspace{14mu} l_{m}}} = \left\{ {{n\mspace{14mu} {surrounding}\mspace{14mu} {and}\mspace{14mu} {including}\mspace{14mu} n_{{demand},m}\mspace{14mu} {s.t.\mspace{14mu} P_{C,n}}} > 0} \right\}}\mspace{79mu} {if}{{n_{{demand},{m + 1}} \neq {Ø\text{:}\mspace{14mu} l_{m + 1}}} = \left\{ {{n\mspace{14mu} {surrounding}\mspace{14mu} {and}\mspace{14mu} {including}\mspace{14mu} n_{{demand},{m + 1}}\mspace{14mu} {s.t.\mspace{14mu} P_{C,n}}} > 0} \right\}}}} & (11) \\ {\mspace{79mu} {{for}\mspace{79mu} {{n \in {\left\{ {l_{m}\bigcup l_{m + 1}} \right\} \text{:}\mspace{14mu} A_{n}}} = {P_{C,n} \times \left( {{I\; S\; T_{n + 1}} - {I\; S\; T_{n}}} \right)}}}} & (12) \\ {\mspace{79mu} {{for}{{n = 0},1,\ldots \mspace{14mu},{{55\text{:}\mspace{14mu} C_{C,n}} = \left\{ \begin{matrix} {{C_{{demand},m} \times \left( \frac{A_{n}}{\sum\limits_{i \in l_{m}}A_{i}} \right) \times K},} & {{{if}\mspace{14mu} n} \in l_{m}} \\ {{C_{{demand},{m + 1}} \times \left( \frac{A_{n}}{\sum\limits_{i \in l_{m + 1}}A_{i}} \right) \times K},} & {{{if}\mspace{14mu} n} \in l_{m + 1}} \\ {0,} & {otherwise} \end{matrix} \right.}}}} & (13) \end{matrix}$

-   -   -   For the next iteration:

if P _(c,0)>P_(demand,m) :P _(demand,m) =P _(C,0)  (14)

P _(demand,m+1) =P _(C,n) _(demand,m+1)   (15)

-   -   6. At the start of a month:

P _(demand,m)=0  (16)

P _(demand,m+1)=0  (17)

W _(T1-HLH,m) =W _(T1-HLH,m+1)  (18)

-   -   7. Repeat computations under point 5.

Exaggerated Example for One Iteration at a Given Time:

-   -   FIG. 94 is a graph 9400 illustrating an example for one         iteration at a given time.     -   n_(demand,m)=4 and n_(demand,m+1)=14 (assuming         P_(C,14)=max(P_(C,n)) for nεm+1)     -   l_(m)={2,3,4,5} and l_(m+1)={14}

P _(C,2)=TFS₂ −P _(th,m,2)

C _(C,2) =C _(demand,m) ×[A ₂/(A ₂ +A ₃ +A ₄ +A ₅)]×K

P _(C,3)=TFS₃ −P _(th,m,3)

C _(C,3) =C _(demand,m) ×[A ₃/(A ₂ +A ₃ +A ₄ +A ₅)]×K

P _(C,4)=TFS₄ −P _(th,m,4)

C _(C,4) =C _(demand,m) ×[A ₄/(A ₂ +A ₃ +A ₄ +A ₅)]×K

P _(C,5)=TFS₅ −P _(th,m,5)

C _(C,5) =C _(demand,m) ×[A ₅/(A ₂ +A ₃ +A ₄ +A ₅)]×K

P _(C,10)=TFS₁₀ −P _(th,m,10)

C _(C,10)=0

P _(C,14)=TFS₁₄ −P _(th,m+1,14)

C _(C,14) =C _(demand,m+1) ×[A ₁₄ /A ₁₄ ]×K=C _(demand,m+1) ×K

-   -   For the next iteration, P_(demand,m)=0 and         P_(demand,m+1)=P_(C,14).

6.3.22 Incentive Function BPA Demand Charges (Function 7.1.1) Description

An evaluation of prior function 7.1 BPA Demand Charges was carried out. This evaluation determined that the function was not recognizing meaningful events in the presence of real load data (precisely, transactive feedback signals (TFS) data). While the inputs specified for this present function 7.1.1 have not changed from those in function 7.1, the pseudo code algorithm has been significantly simplified and has been shown through simulation to properly identify new demand peaks and their cost impacts.

This function predicts the impact of demand charges that the Bonneville Power Administration (BPA) will apply to its customer utilities according to interpretation of its intricate Tiered Rate Methodology (TRM). The TRM explains how BPA's demand charges are to be allocated to its customer utilities at the conclusion of each month. However, since the transactive control and coordination system is predictive, the demand charge impacts of the methodology should be predicted instead. This function can, at best, estimate the demand charge impacts from the TRM.

Many components of the TRM duplicate energy costs that will already be represented in the transactive incentive signal (TIS) by electrically upstream locations. Generally, transactive control applies energy costs at the points where electrical energy is generated and fed into the electrical power grid. These influences should not be duplicated or double-counted. Therefore, this function should only insert the unique demand charge impacts from the TRM that apply specifically to the utilities. This may be achieved by applying upward pressure to the TIS—by adding, to the TIS computation, the product of the pair of capacity cost C_(C) and incremental demand P_(C)—as the transactive feedback signal (TFS) predicts the occurrence of a peak that exceeds the highest peak that has already been recorded during the present calendar month prior to the start time (e.g., IST₀) of the TFS. If the increased TIS, in turn, induces responsive assets to curtail load, the predicted peak may be lowered enough to prevent any additional demand charge.

Normally, an electric utility would be the entity to apply this function. This function applies to a “utility” transactive node or to the transactive node that represents the perspective of an electric utility.

Block Input/Output Function Model: Inputs:

-   -   HLH—[number of hours]—Heavy Load Hour for every month of the         year. The daily HLH periods are defined by the North American         Electric Reliability Corporation (NERC), but should be updated         yearly or whenever there is a change. Presently, HLH hours are         defined between 6:00 am and 10:00 pm (prevailing Pacific Time)         on days excluding Sundays and recognized holidays.     -   C_(demand)—[$/kW]—BPA demand rate for every month for two years,         as approved by Federal Energy Regulatory Commission (FERC) and         published by BPA at the beginning of every other fiscal year         (starting in October). This is to be updated every other year in         this function or whenever there is a change.     -   CDQ—[kW]—Contract Demand Quantity for every month of the year,         as computed yearly by BPA for each of the customer/utility. This         is to be updated yearly in this function or whenever there is a         change.     -   W_(T1-HLH,m0)—[kWh]—planned Tier 1 HLH energy for the present         month m when this function is employed for the very first time;         this is an initial condition.     -   W_(T1-HLH,m+1)—[kWh]—planned Tier 1 HLH energy for the upcoming         month m+1. Since the transactive control and coordination system         provides predictions for a 4-day horizon, this should be made         available to this function at least 4 days prior to the start of         an upcoming month.     -   P_(nonFed) _(—) _(fix) (optional)—[kW]—monthly fixed non-Federal         power capacity that is planned by or contracted to a customer.         This may include Tier 2 power capacity, Secondary Credit         Service, Super Peak Credit, and any other energy resources that         qualify as fixed non-Federal. This is to be updated whenever         there is a change in the planned or contracted power capacity.     -   P_(nonFed) _(—) _(var) (optional)—[kW]—monthly variable         non-Federal power capacity that is available to a customer. An         example is small-scale renewables resources, which may be         considered as negative loads. Due to the variability of such         resources, their power outputs will have to be predicted, for         each of IST intervals, to be used in this function. P_(nonFed)         _(—) _(var) may be set to zero if it is small 5%) compared to         P_(nonFed) _(—) _(fix).     -   TFS_(n)—[kW]—Transactive Feedback Signal, which is available         within the transactive node framework, where, for example, n=0,         1, . . . , 55. The TFS is only to be used when it represents the         total load for the customer's service territory. If that is not         the case, the customer should use a secondary source for the         prediction of its total utility load.     -   IST_(n)—Present time series interval start times used by the         toolkit framework, where, for example, n=0, 1, . . . , 56.         (There is no prediction to correspond with IST_(n) for n=56.         This last IST is simply used to make it clear when the final         interval concludes.)     -   K—dimensionless—scaling factor (a constant) by which the effect         of the demand charge on the TIS may be scaled. This is to be set         to 1.0 until it becomes clear that it will be used.

Interim Calculation Products:

-   -   aHLH—[kW]—average monthly Tier 1 load served during the HLH of         the month. This value is determined by dividing a month's HLH         energy by the number of HLH hours that month.     -   P_(th)—[kW]—threshold power capacity above which demand charge         may be incurred. This value is determined as a sum of other         stated BPA threshold values.     -   P_(demand)—[kW]—Demand amount/capacity at a given point in a         month. This is updated every time a higher demand capacity is         recorded. At the end of the month, this should be the same as         the usual demand amount—also defined as the Demand Charge         Billing Determinant—which is used to compute the customer's         demand charge for the month.     -   P′_(demand)—[kW]—Like P_(demand), but refers to the predicted         future.

Outputs:

-   -   P_(C,n)—[kW]—average power capacity, corresponding to IST_(n).         This output parameter increases each time a new monthly peak         demand occurs or is predicted to occur. By the end of a month,         the sum of P_(C,1) parameters should be very close to the         determinant upon which BPA demand charges are calculated.     -   C_(C,n)—[$/kW]—capacity costs, corresponding to IST_(n). This         parameter is defined nonzero at times P_(C,n) is nonzero. The         magnitude of C_(C,n) is constant during a month, equal to the         rate that is to be charged by BPA that month for utility demand.

Pseudo Code Implementation:

a. Beginning of New Month:

-   -   Set m=0     -   Initialize P_(th)(ThisMonth), C_(demand)(ThisMonth),         P_(th)(NextMonth), C_(demand)(NextMonth) based on tabular         contract information from the energy supplier for the present         and next month     -   Set P_(demand)(m)=P_(th)(ThisMonth)         b. Beginning of Update Interval:     -   Set m=m+1         c. Beginning of Relaxation Update:     -   Set P′_(demand)(ThisMonth)=P_(demand)(m−1)     -   Set P′_(demand)(NextMonth)=P_(th)(NextMonth)     -   Calculate TFS_(n)(m) for n={0, 2, . . . 55}     -   For n=0 to 55

If TFS_(n)(m) > P′_(demand)(ThisMonth)  AND IST_(n)(m) is in ThisMonth  AND IST_(n)(m) is within HLH hours, then   Set P_c_(n)(m) = TFS_(n)(m) − P′_(demand)(ThisMonth)   Set C_c_(n)(m) = C_(demand)(ThisMonth)   Set P′_(demand)(ThisMonth) = TFS_(n)(m) Elseif TFS_(n)(m) > P′_(demand) (NextMonth)  AND IST_(n)(m) is in NextMonth  AND IST_(n)(m) is within HLH hours, then   Set P_c_(n)(m) = TFS_(n)(m) − P′_(demand)(NextMonth)   Set C_c_(n)(m) = C_(demand)(NextMonth)   Set P′_(demand)(NextMonth) = TFS_(n)(m) Else   Set P_c_(n)(m) = 0   Set C_c_(n)(m) = 0 Next n d. On next relaxation update (e.g., IST₀=IST₁(m,0) does not change, but a new calculation ID is in effect)

-   -   Go to (c)         e. On next update interval (e.g., IST₀=IST₀(m+1) will advance 5         minutes and a new calculation ID is in effect),     -   If IST₀(m) is in HLH hours, then

  Set P_(demand)(m) = maximum(P_(demand)(m−1), TFS₀(m)) Else   Set P_(demand)(m) = P_(demand)(m−1) End

-   -   Go to (b)         f. On next month (e.g., IST₀ is in NextMonth and a new         calculation ID is in effect)     -   Go to (a)

APPENDIX A Mat lab code that implements the stated pseudo code for TKRS_7.1.1 % Note that function lnHLH(a) has been created. It returns a Boolean true % if time a is within HLH hours and is not a Sunday. The format of % variable “a” is presumed to be ‘yyyy-mm-ddTHH:MM:SS’. NextMonth = ThisMonth+1; % (a) Beginning of New Month PeakMonthDemand(1) = DemandThreshold(ThisMonth); P_c(1,1:56) = zeros(1,56); C_c(1,1:56) = zeros(1,56); for m = 2:length(TFS(:,1))  % (b) Beginning of Update Interval  if ~strcmp(IST(m,1),IST(m−1,1)) && lnHLH(IST(m−1,1)) % An  update interval   PeakMonthDemand(m) = max(PeakMonthDemand(m−1),  TFS(m−1,1));  else % A relaxation update   PeakMonthDemand(m) = PeakMonthDemand(m−1);  end  % (c) Beginning of Relaxation Update:  PeakFutureDemand(ThisMonth) = PeakMonthDemand(m−1);  PeakFutureDemand(NextMonth) = DemandThreshold(NextMonth);  % TFS was already read from project data  for n = 1:56 % NOTE: No distinction made yet for month    if TFS(m,n) > PeakFutureDemand(ThisMonth) ...       && datenum(IST(m,n),DF) >=       datenum([num2str(DemandRateYear(ThisMonth)),‘-       ’,num2str(DemandRateMonth(ThisMonth)),‘−01’]) ...       && datenum(IST(m,n),DF) <       datenum([num2str(DemandRateYear(NextMonth)),‘-       ’,num2str(DemandRateMonth(NextMonth)),‘−01’]) ...     && lnHLH(IST(m,n));     P_c(m,n) = TFS(m,n) − PeakFutureDemand(ThisMonth);     C_c(m,n) = DemandCharge(ThisMonth);     PeakFutureDemand(ThisMonth) = TFS(m,n);    elseif TFS(m,n) > PeakFutureDemand(NextMonth) ...       && datenum(IST(m,n),DF) >=       datenum([num2str(DemandRateYear(NextMonth)),‘-       ’,num2str(DemandRateMonth(NextMonth)),‘−01’]) ...     && lnHLH(IST(m,n));     P_c(m,n) = TFS(m,n) − PeakFutureDemand(NextMonth);     C_c(m,n) = DemandCharge(NextMonth);     PeakFutureDemand(NextMonth) = TFS(m,n);    else     P_c(m,n) = 0;     C_c(m,n) = 0;    end  end end; ************************************************************ ************************************* function [YorN] = lnHLH(T) % lnHLH--Logic check true if in HLH hours DT = ‘yyyy-mm-ddTHH:MM:SS’; T = datenum(T,DT) − (7 + (datenum(T,DT) > datenum(2012,11,4,10,0, 0)))/24; YorN = weekday(T) ~= 1 ...  && datevec(T)*[0 0 0 1 0 0]’ >= 6 && datevec(T)*[0 0 0 1 0 0]’ < 22;

FIG. 95 is a diagram 9500 that shows the specified strategy during a month.

6.3.23 Incentive Function Seattle City Light Demand Charges (Function 7.2) Description

This function predicts the impacts of energy and demand charges that the Seattle City Light (SCL) will apply to the University of Washington (UW). SCL supplies the UW with most of its electricity.

This function applies the impact of its energy charges to the weighted cost for the total energy imported into UW's energy territory from the TZ02 (West Washington) transmission zone transactive node. This is achieved through the addition of an “other” cost component C_(O) to the numerator of the TIS computation at UW's transactive node.

Although the SCL's demand charges are to be allocated at the conclusion of each month, since the transactive control and coordination system is predictive, the demand charge impacts should be predicted instead. This function can, at best, estimate the demand charge impacts. UW expects to minimize its monthly SCL demand changes by using this function to apply upward pressure to its TIS when its transactive feedback signal (TFS) predicts the occurrence of a peak in its load. This is achieved by adding the product of the pair of capacity cost C_(C) and average power capacity P_(C) to the numerator of its TIS computation whenever its TFS exceeds the highest peak that has already been recorded during the time elapsed for the calendar month prior to the start time (e.g., IST₀). The product C_(C)·P_(C) thus represents the incremental demand charge that UW would incur if a new peak were to happen. If the increased TIS, in turn, applies enough downward pressure on the TFS (e.g. through load curtailment), the predicted peak may be lowered enough to prevent any additional demand charge. It should be noted that, because the SCL demand charges apply to the maximum demand during the month, the charges can only be minimized and not completely eliminated. This function is to be applied at UW's transactive node.

Block Input/Output Function Model: Inputs:

-   -   C_(energy) _(—) _(peak)—[$/kWh]—SCL peak energy rate. This peak         energy rate is to be updated whenever there is a change. This         rate applies to energy used between six (6:00) a.m. and ten         (10:00) p.m., Monday through Saturday, excluding major holidays.         (Major holidays excluded from the peak period are New Year's         Day, Memorial Day, Independence Day, Thanksgiving Day, and         Christmas Day.) Note that Sunday is considered as part of the         off-peak period. This rate is $0.0638/kWh in one example.     -   C_(energy) _(—) _(offpeak)—[$/kWh]—SCL off-peak energy rate.         This off-peak energy rate is to be updated whenever there is a         change. This rate applies to energy used during times other than         the peak period. This rate is $0.0432/kWh in one example.     -   C_(demand) _(—) _(peak)—[$/kW]—SCL peak demand rate. This peak         demand rate is to be updated whenever there is a change. This         rate applies to kW of maximum demand P_(max) _(—) _(peak) during         the peak period. This rate is $0.98/kW in one example.     -   C_(demand) _(—) _(offpeak)—[$/kW]—SCL off-peak demand rate. This         off-peak demand rate is to be updated whenever there is a         change. This rate applies to kW of maximum demand P_(max) _(—)         _(offpeak) in excess of P_(max) _(—) _(peak) during times other         than the peak period. In other words, this off-peak demand rate         applies only if P_(max) _(—) _(offpeak)>P_(max) _(—) _(peak) and         applies only to the difference P_(max) _(—) _(offpeak)−P_(max)         _(—) _(peak). This rate is $0.26/kW in one example.     -   {P_(peak1), P_(peak2), . . . , P_(peak12)}—[kW]—monthly peak         load for every month of the most recent elapsed year. This is to         be updated at the beginning of a year if more recent data become         available.     -   K—dimensionless—scaling factor (a constant) by which the effect         of the demand charge on the TIS may be scaled. This is to be set         to 1.0 until it becomes clear that it will be used.     -   TFS_(n)—[kW]—Transactive Feedback Signal, which is available         within the transactive node framework, corresponding to the         n^(th) interval, where, for example, n=0, 1, . . . , 55. The TFS         is only to be used if it represents the total UW's demand from         SCL.     -   IST_(n)—Present time series interval start times used by the         toolkit framework. (There is no prediction to correspond with         IST₅₆. This last IST is simply used to make it clear when the         final interval concludes.)     -   TFS_(TZ02,n)—[kW]—Transactive Feedback Signal from transmission         zone transactive node TZ02, representing the average power         imported into UW's service territory during the n^(th) interval.

Interim Calculation Products:

-   -   P_(max) _(—) _(peak)—[kW]—Maximum demand during the peak period.     -   P_(max) _(—) _(offpeak)—[kW]—Maximum demand during the off-peak         period.     -   h_(peak)—[number of hours]—Number of hours in one continuous         peak period.     -   h_(offpeak)—[number of hours]—Number of hours in one continuous         off-peak period.     -   δ_(peak)—[$/kWh]—Adjustment to the weighted cost of imported         energy, due to the impact of SCL energy charges, during peak         period.     -   δ_(offpeak)—[$/kWh]—Adjustment to the weighted cost of imported         energy, due to the impact of SCL energy charges, during off-peak         period.

Outputs:

-   -   C_(O,n)—[$]—Other cost representing SCL's energy charge impact,         corresponding to the n^(th) interval.     -   C_(C,n)—[$/kW]—Capacity cost corresponding to the n^(th)         interval.     -   P_(C,n)—[kW]—Average power capacity corresponding to the n^(th)         interval.

Pseudo Code Implementation:

∀n,C _(O,n)=(x·δ _(peak) +y·δ _(offpeak))·TFS_(TZ02,n) ·Δt _(n),  (4)

where

x=portion/fraction of n lying within l _(peak)

y=portion/fraction of n lying within l _(offpeak)  (5)

and

Δt _(n)=IST_(n+1)−IST_(n).  (6)

-   -   1. Compute P_(C,n):

∀n,P _(C,n)=max(0,TFS_(n) −P _(max) _(—) _(peak))  (7)

-   -   2. Compute C_(C,n):

$\begin{matrix} {{\forall n},{C_{C,n} = \left\{ \begin{matrix} {{K \cdot \left( {{x \cdot C_{demand\_ peak}} + {y \cdot C_{demand\_ offpeak}}} \right)},} & {{{if}\mspace{14mu} T\; F\; S_{n}} > P_{max\_ offpeak} > P_{max\_ peak}} \\ {{K \cdot x \cdot C_{demand\_ peak}},} & {{{if}\mspace{14mu} T\; F\; S_{n}} > P_{max\_ peak} \geq P_{max\_ offpeak}} \\ {0,} & {otherwise} \end{matrix} \right.}} & (8) \end{matrix}$

-   -   -   where x and y are as defined in equation (5) above.

    -   3. For the next iteration:

$\begin{matrix} {P_{max\_ peak} = \left\{ \begin{matrix} {{T\; F\; S_{0}},} & {{{if}\mspace{14mu} \left( \; {{T\; F\; S_{0}} > P_{max\_ peak}} \right)}\bigcap\left( {n = {0 \in l_{peak}}} \right)} \\ {P_{max\_ peak},} & {otherwise} \end{matrix} \right.} & (9) \\ {P_{max\_ offpeak} = \left\{ \begin{matrix} {{T\; F\; S_{0}},} & {{{if}\mspace{14mu} \left( {{T\; F\; S_{0}} > P_{max\_ offpeak}} \right)}\bigcap\left( {n = {0 \in l_{offpeak}}} \right)} \\ {P_{max\_ offpeak},} & {otherwise} \end{matrix} \right.} & (10) \end{matrix}$

-   -   4. Repeat, starting from point 6.

Example

-   -   h_(peak)=16 h_(offpeak)=8     -   C_(energy) _(—) _(peak)=$0.0638/kWh C_(energy) _(—)         _(offpeak)=$0.0432/kWh     -   δ_(peak)=$0.0069/kWh δ_(offpeak)=−$0.0137/kWh     -   C_(demand) _(—) _(peak)=$0.98/kW C_(demand) _(—)         _(offpeak)=$0.26/kW     -   K=1.0     -   P_(max) _(—) _(peak)=P_(max) _(—) _(offpeak)=min(P_(peak1),         P_(peak2), . . . , P_(peak12))×90%=40000 kW×90%=36000 kW

Δt_(n) x y TFS_(n) TFS_(TZ02,n) TIS_(TZ02,n) C_(O,n) P_(C,n) C_(C,n) TIS_(n) n IST_(n) [h] [%] [%] [kW] [kW] [$/kWh] [$] [kW] [$/kW] [$/kWh] 0 9/1/10 1/12 0 100 31225 31225 0.0569 −35.74 0 0.00 0.0432 0:00 1 9/1/10 1/12 0 100 31200 31200 0.0569 −35.71 0 0.00 0.0432 0:05 2 9/1/10 1/12 0 100 31199 31199 0.0569 −35.71 0 0.00 0.0432 0:10 3 9/1/10 1/12 0 100 31192 31192 0.0569 −35.70 0 0.00 0.0432 0:15 4 9/1/10 1/12 0 100 31199 31199 0.0569 −35.71 0 0.00 0.0432 0:20 5 9/1/10 1/12 0 100 31195 31195 0.0569 −35.70 0 0.00 0.0432 0:25 6 9/1/10 1/12 0 100 31192 31192 0.0569 −35.70 0 0.00 0.0432 0:30 7 9/1/10 1/12 0 100 31090 31090 0.0569 −35.58 0 0.00 0.0432 0:35 8 9/1/10 1/12 0 100 31100 31100 0.0569 −35.59 0 0.00 0.0432 0:40 9 9/1/10 1/12 0 100 31112 31112 0.0569 −35.61 0 0.00 0.0432 0:45 10 9/1/10 1/12 0 100 31090 31090 0.0569 −35.58 0 0.00 0.0432 0:50 11 9/1/10 1/12 0 100 31000 31000 0.0569 −35.48 0 0.00 0.0432 0:55 12 9/1/10 ¼ 0 100 30960 30960 0.0569 −106.30 0 0.00 0.0432 1:00 13 9/1/10 ¼ 0 100 31000 31000 0.0569 −106.43 0 0.00 0.0432 1:15 14 9/1/10 ¼ 0 100 30936 30936 0.0569 −106.21 0 0.00 0.0432 1:30 15 9/1/10 ¼ 0 100 30904 30904 0.0569 −106.10 0 0.00 0.0432 1:45 16 9/1/10 ¼ 0 100 30880 30880 0.0569 −106.02 0 0.00 0.0432 2:00 17 9/1/10 ¼ 0 100 30784 30784 0.0569 −105.69 0 0.00 0.0432 2:15 18 9/1/10 ¼ 0 100 30880 30880 0.0569 −106.02 0 0.00 0.0432 2:30 19 9/1/10 ¼ 0 100 30848 30848 0.0569 −105.91 0 0.00 0.0432 2:45 20 9/1/10 ¼ 0 100 30816 30816 0.0569 −105.80 0 0.00 0.0432 3:00 21 9/1/10 ¼ 0 100 30776 30776 0.0569 −105.66 0 0.00 0.0432 3:15 22 9/1/10 ¼ 0 100 30760 30760 0.0569 −105.61 0 0.00 0.0432 3:30 23 9/1/10 ¼ 0 100 30672 30672 0.0569 −105.31 0 0.00 0.0432 3:45 24 9/1/10 ¼ 0 100 30672 30672 0.0569 −105.31 0 0.00 0.0432 4:00 25 9/1/10 ¼ 0 100 30768 30768 0.0569 −105.64 0 0.00 0.0432 4:15 26 9/1/10 ¼ 0 100 30760 30760 0.0569 −105.61 0 0.00 0.0432 4:30 27 9/1/10 ¼ 0 100 30872 30872 0.0569 −105.99 0 0.00 0.0432 4:45 28 9/1/10 ¼ 0 100 31016 31016 0.0569 −106.49 0 0.00 0.0432 5:00 29 9/1/10 ¼ 0 100 31584 31584 0.0569 −108.44 0 0.00 0.0432 5:15 30 9/1/10 ¼ 0 100 31848 31848 0.0569 −109.34 0 0.00 0.0432 5:30 31 9/1/10 ¼ 0 100 32072 32072 0.0569 −110.11 0 0.00 0.0432 5:45 32 9/1/10 1 100 0 32986 32986 0.0569 226.50 0 0.00 0.0638 6:00 33 9/1/10 1 100 0 34300 34300 0.0569 235.53 0 0.00 0.0638 7:00 34 9/1/10 1 100 0 35476 35476 0.0569 243.60 0 0.00 0.0638 8:00 35 9/1/10 1 100 0 36876 36876 0.0569 253.22 876 0.98 0.0870 9:00 36 9/1/10 1 100 0 38084 38084 0.0569 261.51 2084 0.98 0.1174 10:00  37 9/1/10 1 100 0 38750 38750 0.0569 266.08 2750 0.98 0.1333 11:00  38 9/1/10 1 100 0 39536 39536 0.0569 271.48 3536 0.98 0.1514 12:00  39 9/1/10 1 100 0 39618 39618 0.0569 272.04 3618 0.98 0.1533 13:00  40 9/1/10 1 100 0 39962 39962 0.0569 274.41 3962 0.98 0.1609 14:00  41 9/1/10 1 100 0 40140 40140 0.0569 275.63 4140 0.98 0.1648 15:00  42 9/1/10 1 100 0 39682 39682 0.0569 272.48 3682 0.98 0.1547 16:00  43 9/1/10 1 100 0 38194 38194 0.0569 262.27 2194 0.98 0.1201 17:00  44 9/1/10 1 100 0 36804 36804 0.0569 252.72 804 0.98 0.0852 18:00  45 9/1/10 1 100 0 35284 35284 0.0569 242.28 0 0.00 0.0638 19:00  46 9/1/10 1 100 0 34742 34742 0.0569 238.56 0 0.00 0.0638 20:00  47 9/1/10 1 100 0 33852 33852 0.0569 232.45 0 0.00 0.0638 21:00  48 9/1/10 1 0 100 32612 32612 0.0569 −447.87 0 0.00 0.0432 22:00  49 9/1/10 1 0 100 31578 31578 0.0569 −433.67 0 0.00 0.0432 23:00  50 9/2/10 6 0 100 30497 30497 0.0569 −2512.98 0 0.00 0.0432 0:00 51 9/2/10 6 100 0 35836 35836 0.0569 1476.46 0 0.00 0.0638 6:00 52 9/2/10 6 100 0 40813 40813 0.0569 1681.51 4813 0.98 0.0830 12:00  53 9/2/10 6 67 33 34919 34919 0.0569 0.00 0 0.00 0.0569 18:00  54 9/3/10 24  67 33 36143 36143 0.0569 0.00 143 0.65 0.0570 0:00 5 9/4/10 24  67 33 31816 31816 0.0569 0.00 0 0.00 0.0569 0:00

For the next iteration, P_(max) _(—) _(peak)=36000 kW and P_(max) _(—) _(offpeak)=36000 kW.

In the above table, TFS_(TZ02)=TFS, but some mismatch should be expected in reality. TIS_(TZ02) is not required as an input to this function. It is simply being used in this example for the computation of TIS. It is shown as constant here, but is more like to have some variation in reality. TIS is neither an output of or input to this function, but is given in this example to show the impacts of both SCL energy and demand charges. In certain embodiments, TIS can be computed as follows:

${T\; I\; S_{n}} = \frac{{T\; I\; {S_{{{TZ}\; 02},n} \cdot T}\; F\; {S_{{{TZ}\; 02},n} \cdot \Delta}\; t_{n}} + {C_{C,n} \cdot P_{C,n}} + C_{O,n}}{T\; F\; {S_{{{TZ}\; 02},n} \cdot \Delta}\; t_{n}}$

6.3.24 Incentive Function Spot Market Impacts (Function 8.1) Description

This function is to be used by a utility that wishes to mitigate the impacts that it will likely incur in spot markets. This function modifies the transactive incentive signal so that the utility's resources may help the utility respond to its participation in the spot market.

Refer to FIG. 96 is a graph 9600 illustrating power operations concepts. that will be useful as some basic components of a utilities' power mix are addressed. For utilities that trade on the spot market, the cost of procured energy is the sum of costs from these following components:

Base load—large blocks of constant capacity that will have been procured far in advance of the day on which it will be used.

Term trading—procurement of coarsely shaped energy supply far in advance of the day on which it will be used.

Pre-scheduled trading—procurement of well-shaped energy supply that should be settled no later than the morning before the day on which the energy will be used.

Spot market trading—procurement of “real-time” energy needs that should be settled just shortly before the beginning of the hour in which the energy will be used. The purpose of this trading is to obtain an accurate, final balance between forecasted load and energy resources. The energy traded on a spot market is among the most expensive energy resources in a utility's resource mix. Surplus energy may be sold in the spot market. A spot market usually addresses hourly periods, but a trend has begun to shorten the intervals to 30 minutes or even shorter.

The transactive signals calculated at a transactive node will have incorporated the costs and energy from base load, term, and some of pre-scheduled energy resources that will be known from published schedules. However, the resources procured from “real-time” spot market trading may not be predictable far in advance. Furthermore, the strategies and trades may not be revealed by traders due to regulations and the business sensitivity of this information.

This function specifies two mechanisms by which the impacts of spot market trading should influence the transactive incentive signal:

-   -   1. Cost of energy procured on the spot market. As for any energy         resource, the cost of energy procured on the spot market will         have an impact on the delivered cost of energy commensurate with         the fraction of total load that this energy represents. A         transactive node should predict the energy that it will procure         on the spot market and the cost of that energy. Normally, this         prediction will become more accurate as an affected hour draws         near. This effect produces energy terms C_(E) and P_(G) into the         algorithmic framework at a transactive node. Because only a         small fraction of a transactive node's forecasted load is         supplied by spot market trading, the influence of the cost of         energy procured on the spot market will also be relatively         small. (For example, if the average unit cost of other resources         is $10/MWh and the spot market unit cost is $50/MWh for 5% of         the total forecasted load, the resulting weighted unit cost is         $12/MWh.)         -   The calculation of a TIS is performed presently by summing             the costs and quantities of imported or generated energy,             not of exported or consumed energy. Therefore, the cost of             any energy that is sold (e.g., that will be exported) in a             spot market has no impact on the TIS.     -   2. An additional incentive from the utility to incentivize         responsive assets to respond to the relative cost of energy on         the spot market. From a utility's perspective, its customers         should defer energy consumption from times at which spot market         energy is expensive to times at which it is inexpensive. This         statement is true both at times that energy should be purchase         and sold on the spot market. Therefore, another incentive         component can be used to induce a utility's customers to respond         to the relative cost of energy on the spot market.         -   This incentive should create no net change in the delivered             cost of energy over long periods of time; for each hour that             it disincentivizes consumption it should create an hour             during which it incentivizes consumption to a similar             degree. Because the outcome of this incentive should be a             benefit (or cost) for an hourly block of time, this function             will assert that the infrastructure cost term C_(I) (units:             $/h) should be used to represent this incentive.

Block Input/Output Function Model: Inputs:

-   -   {P(h₀−1), P(h₀−2), . . . , P(h₀−i), . . . ,         P(h₀−I)}—[kW]—historical time series of traded capacity for         recent spot market trading hours h₀−i     -   {C(h₀−1), C(h₀−2), . . . , C(h₀−i), . . . ,         C(h₀−I)}—[$/kWh]—historical time series of unit energy cost from         prior recent prior spot market trading at hours h−1, h−2, etc.     -   {P(h₀), P(h₀+1), . . . P(h₀+i), . . . , P(h₀+I)}—[kW]—predicted         hourly capacity shortfall or surplus for each hour of the next         four days (e.g., the predicted time horizon of the transactive         signals), to the degree that such shortfalls and surpluses may         be known. Where this input cannot be known, trends will be used.         Where this input is known, it may be used to improve the         trending predictions.     -   {C(h₀), C(h₀+1), . . . , C(h₀+i), . . . ,         C(h₀+I)}—[$/kWh]—predicted hourly unit cost of energy that may         be purchased in the spot market for each hour of the next four         days (e.g., the predicted time horizon of the transactive         signals), to the degree that such shortfalls and surpluses may         be known. Where this input cannot be known, trends will be used.         Where this input is known, it may be used to improve the         trending predictions.     -   K—dimensionless—scaling parameter (a constant) by which effect         of utility incentive on C_(I) may be scaled.

Interim Calculation Products:

-   -   C_(trend, all)—[$/kWh]—average historical spot market unit         energy cost     -   |P|_(ave)—[kW]—average magnitude of historical procured (or         sold) spot market capacity     -   {C_(trend,1), C_(trend,2), . . . , C_(trend, h), . . . ,         C_(trend,24)}—[$/KWh]—trended spot market unit energy cost by         hour of day     -   {P_(trend,1), P_(trend,2), . . . , P_(trend,h), . . . ,         P_(trend,24)}—[kW]—trended procured (or sold) spot market         capacity by hour of day

Outputs:

-   -   {P_(G,0), P_(G,1), . . . , P_(G,n), . . . ,         P_(G,N)}—[kW]—predicted average power that is predicted to be         purchased or sold during each IST interval of the current IST         series. (The sign convention should apply a positive number to         sold capacity and negative number to purchased capacity.) (In         certain embodiments, there will be 56 IST intervals.)     -   {C_(E,0), C_(E,1), . . . , C_(E,n), . . . ,         C_(E,N)}—[$/kWh]—predicted unit energy cost of energy that is         predicted to be purchased or sold on the spot market during each         IST_(n) interval of the current IST series     -   {C_(I,0), C_(I,1), . . . , C_(I,n), . . . ,         C_(E,N)}—[$/h]—predicted hourly incentive applied to induce         customers to track relative spot market pricing for each         interval n.

Pseudo Code Implementation:

-   -   1. Convert available historical and predicted power capacities         into the units kW.     -   2. Convert available historical and predicted unit energy costs         into the units $/kWh.     -   3. Calculate or update the average historical spot market unit         energy cost (e.g., its trend)

$\begin{matrix} {C_{{trend},{all}} = {\frac{1}{H}{\sum\limits_{i = 1}^{H}{C\left( {h_{0} - i} \right)}}}} & (1) \end{matrix}$

-   -   -   C_(trend,all)—[$/kWh]—average historical spot market unit             energy cost for all hours of the day         -   H—dimensionless—total number of historic hours used in this             calculation         -   C(h₀−i)—[$/kWh]—spot market unit price of energy observed             from historic hour h₀−i.

    -   In subsequent updates, this value may be updated each hour by         applying the following filter to the prior calculation result:         (The number 168 is the number of hours in a week. This number         sets the dynamics with which the average spot market price will         be tracked.)

$\begin{matrix} {C_{{trend},{all}} = \frac{{167 \cdot C_{{trend},{all},{old}}} + {C\left( h_{0} \right)}}{168}} & (2) \end{matrix}$

-   -   -   C_(trend,all,old)—[$/kWh]—the representation of the average             spot market price that has incorporated spot market prices             prior to C(h₀). This is the prior value C_(trend all) that             existed before this update.

    -   4. Calculate or update the average magnitude of historical spot         market capacity (e.g., its trend)

$\begin{matrix} {{P}_{ave} = {\frac{1}{H}{\sum\limits_{i = 1}^{H}{{P\left( {h_{o} - i} \right)}}}}} & (3) \end{matrix}$

-   -   -   |P|_(ave)—[kW]—average magnitude of historical spot market             capacity for energy that has been procured or sold for all             hours of the day         -   H—dimensionless—total number of historic hours used in this             calculation         -   |P(h₀−i)|—[kW]—magnitude of spot market capacity procured or             sold in historic hour h₀−i.

    -   In subsequent updates, this value may be updated each hour by         applying the following filter to the prior calculation result.         (The number 168 is the number of hours in a week. This number         sets the dynamics with which the average spot market capacity         purchased or sold will be tracked.):

$\begin{matrix} {{{P}_{ave} = \frac{{167 \cdot {P}_{{ave},{old}}} + {{P\left( h_{0} \right)}}}{168}},} & (4) \end{matrix}$

-   -   -   |P(h₀)|—[kW]—the magnitude of the next spot market capacity             to become known         -   |P|_(ave,old)—[kW]—the average spot market capacity that has             incorporated spot market capacities procured or sold prior             to |P(h₀)|.

    -   5. Calculate or update trends for hour-by-hour spot market unit         energy cost that may be used if better predictions are not         known. For each hour of the day h, estimate the recent average         spot market unit cost of energy. If a utility possesses better         means to make these predictions, then such predictions should         replace trend information as it becomes available.

$\begin{matrix} {C_{{trend},h} = {\frac{1}{D}{\sum\limits_{d = 1}^{D}{C_{h}\left( {d_{0} - d} \right)}}}} & (5) \end{matrix}$

-   -   -   C_(trend,h)—[[$/kWh]—average spot market unit cost of energy             for the last D recent days. At least seven days should be             used.         -   D—[dimensionless]—number of days included in the average             trend         -   C_(h)(d₀−d)—[$/kWh]—the spot market unit energy price for             hour of day h recorded d days prior to the present index day             d₀.

    -   Successive updates may be accomplished using the following         filter that has a response time of about 1 week.

$\begin{matrix} {C_{{trend},h} = \frac{{6 \cdot C_{{trend},h,{old}}} + {C_{h}\left( d_{0} \right)}}{7}} & (6) \end{matrix}$

-   -   -   C_(trend,h,old)—[$/kWh]—prior value of C_(trend,h) that will             become displaced by this update.

    -   6. Calculate or update trends for hour-by-hour spot market         capacity purchased or sold that may be used if better         predictions are not known. If a utility possesses better means         to make these predictions, then such predictions should replace         trend information as it becomes available.

$\begin{matrix} {P_{{trend},h} = {\frac{1}{D}{\sum\limits_{d = 1}^{D}{P_{h}\left( {d_{o} - d} \right)}}}} & (7) \end{matrix}$

-   -   -   P_(trend,h)—[kW]—average spot market capacity that is             procured or sold during hour of day h in the recent history             of this transactive node.         -   D—[dimensionless]—number of days included in the average             trend         -   P_(h) (d₀−d)—[kW]—the spot market capacity procured or sold             for hour of day h recorded d days prior to the present index             day d₀.

    -   Successive updates may be accomplished using the following         filter that has a response time of about 1 week.

$\begin{matrix} {P_{{trend},h} = \frac{{6 \cdot P_{{trend},h,{old}}} + {P_{h}\left( h_{0} \right)}}{7}} & (8) \end{matrix}$

-   -   -   P_(trend,h,old)—[kW]—prior value of P_(trend,h) that will             become displaced by this update.

    -   7. Update the allocation of predicted spot market capacity to         the IST intervals and make this prediction available as an         output of this function into the transactive node's algorithmic         toolkit framework.

$\begin{matrix} {{{P_{G,n} = P_{{trend},h}},{when}}{{I\; S\; T_{n}} \subseteq h}{{\frac{1}{b - a}{\sum\limits_{h = a}^{b}P_{{trend},h}}},{when}}{h \Subset {I\; S\; T_{n}}}} & (9) \end{matrix}$

-   -   -   P_(G,n)—[kW]—Energy term parameter output to toolkit             algorithmic framework for the interval corresponding to             interval IST_(n).

    -   8. Update the allocation of predicted spot market unit cost of         energy to the IST intervals and make this prediction available         as an output of this function into the transactive node's         algorithmic toolkit framework.

$\begin{matrix} {{{C_{E,n} = C_{{trend},h}},{when}}{{I\; S\; T_{n}} \subseteq h}{{\frac{1}{b - a}{\sum\limits_{h = a}^{b}C_{{trend},h}}},{when}}{h \Subset {I\; S\; T_{n}}}} & (10) \end{matrix}$

-   -   -   C_(E,n)—[$/kWh]—energy cost parameter output to toolkit             algorithms framework for the interval corresponding to             IST_(n).

    -   9. Calculate or update the additional incentive.

C _(I,h) =K·|P _(trend,all)|·(C(h)−C _(trend,all))  (11)

-   -   -   C_(I,h)—[$/h]—an incentive for future hour h. The future             time horizon should be at least as long as that of the             current IST interval set (about 4 days)         -   K—dimensionless—scaling parameter. Set this parameter to 1.0             until it becomes clear that it will be used.         -   |P_(ave,all)|—[kW]—the absolute value of the average             capacity that is traded by this utility in the spot market             based on prior history         -   C(h)—[$/kWh]—the best present prediction of the spot market             unit energy cost during future hour h. This will often have             been estimated from the trended value for this hour of the             day, but it may be replaced by better predictions, if such             prediction are available.         -   C_(ave,all)—[$/kWh]—the average spot market unit cost of             energy based on prior history.

    -   10. Allocate the incentive to IST intervals. Now that the         incentive has been predicted on an hour-by-hour basis, this         incentive should be allocated to the set of IST intervals. Two         cases should be considered. Where hour an interval IST_(n) lies         inside hour h, the incentive is simply assigned to the interval         IST_(n). However, if the interval IST_(n) is longer than an hour         and hour h lies within IST_(n), then the incentive for interval         IST_(n) should be stated as the average of the incentives for         the hours h that lie within IST_(n).

$\begin{matrix} {{{C_{I,n} = C_{I,h}},{when}}{{I\; S\; T_{n}} \subseteq h}{{\frac{1\mspace{14mu} {hour}}{b - a}{\sum\limits_{h = a}^{b}C_{I,h}}},{when}}{h \Subset {I\; S\; T_{n}}}} & (12) \end{matrix}$

-   -   -   C_(I,n)—[$/h]—incentive to be applied during future interval             IST_(n)         -   C_(I,h)—[$/h]—hourly incentive for hour h that was             calculated in equation (X) above         -   (b−a)—[h]—number of hours included in interval IST_(n)             starting from hour a and ending hour b.

FIG. 96 is a graph 9600 illustrating power operations concepts.

6.3.25 Incentive Function Non-Transactive Imported Energy (Function 1.1) Description

This function addresses the importation of electrical energy from outside a transactive node from entities that are not themselves transactive nodes—are not participants in this transactive control and coordination system. This function should be applied at transactive nodes that are scheduled to receive bulk electrical energy from outside the boundaries of the transactive control and coordination system. The California-Oregon Intertie is an example of such a connection that could potentially import energy into a transactive control and coordination system.

It is challenging to generalize this function because the non-transactive sources of imported energy are diverse. However, the energy predicted to flow to or from sources will typically have been scheduled by balancing authorities and other entities that are responsible to negotiate the flow of electrical power to and from the sources. Usually, wholesale market forces determine the cost of the scheduled energy, although such costs may not be promptly known from indices or other records and should therefore be predicted from past trends. Therefore, this function is simply represented as a translation of the scheduled energy and its corresponding predicted energy costs into the parameters of the toolkit framework.

FIG. 97 is a diagram 10700 of an exemplary block input/output function model.

Pseudo Code Implementation:

-   -   1. Procure a current power exchange schedule for the exchange         that is being modeled. This schedule should predict the energy         to be exchanged for at least the next three days if it is to be         useful for the entire predicted future of transactive signals.         Some of these schedules will be found to be published daily or         even less frequently.     -   2. Procure the corresponding index or other documentation of         market price (cost) for the exchanged energy. For much of the         power exchanged in the Northwest, the price (cost) may only be         known a day later from published indices. The energy price         (cost) should therefore be predicted from trends or from an         informed simulation.     -   3. If necessary, restate the scheduled power from step #1 in         units of average power, as will be used for parameter P_(G)         (default units: average kW) in the toolkit framework.     -   4. If necessary, restate the price (cost) from step #2 in units         of unit energy cost, as will be used for parameter C_(E)         (default units; $/kWh) in the toolkit framework. (At this point         in the algorithm, the product will be useful, but it will be         stated still using the intervals from the original exchange         schedule.)     -   5. Interpolate the values C_(E)′ and P_(G)′ to recast their         intervals according to the current set of interval start times         (IST) that should have been calculated by the transactive node.         A library of interpolation functions may evolve to perform such         interpolations, but the basic approach should be to interpolate         the average power P_(G) and cost of energy included in each IST         interval C_(E) as is shown in these equations below. “Included         duration” is the part of a scheduled interval that resides         within a given IST interval.

$\begin{matrix} \begin{matrix} {\mspace{79mu} {P_{G} = \frac{{total}\mspace{14mu} {energy}}{I\; S\; T\mspace{14mu} {duration}}}} \\ {= \frac{\sum\limits_{{IST}\mspace{14mu} {duration}}{\left( {{included}\mspace{14mu} {duration}} \right)\left( {{scheduled}\mspace{14mu} {power}} \right)}}{I\; S\; T\mspace{14mu} {duration}}} \end{matrix} & \left( {1.1a} \right) \\ \begin{matrix} {C_{E} = \frac{\left( {{total}\mspace{14mu} {energy}\mspace{14mu} {cost}} \right)}{{total}\mspace{14mu} {energy}}} \\ {= \frac{\sum\limits_{{IST}\mspace{14mu} {duration}}{\left( {{scheduling}\mspace{14mu} {cost}} \right) \cdot \left( {{included}\mspace{14mu} {duration}} \right) \cdot P_{scheduled}}}{\sum\limits_{{IST}\mspace{14mu} {duration}}{\left( {{included}\mspace{14mu} {duration}} \right) \cdot P_{scheduled}}}} \end{matrix} & \left( {1.1b} \right) \end{matrix}$

P_(G)—Series of average power energy terms expected by the toolkit framework (example units: average kW). Series members correspond to IST intervals. C_(E)—Series of energy cost terms expected by the toolkit framework (example units: $/kWh). Series members correspond to IST intervals. Total energy—Interim calculation of total energy that is exchanged over the duration of a given IST interval (example units: kWh). Included duration—The fractional part of a schedule interval that also lies within a given IST interval (example units: seconds). IST duration—The duration of a given IST interval (example units: seconds). In some embodiments, IST intervals are 5 minutes, 15 minutes, 1 hour, 6 hours, or 1 day long. Scheduled cost—The index or market price that corresponds to the energy exchanged during a given scheduled interval (example units: $/kWh). This cost may be obtained through an informed simulation based on historical data and trends. Scheduled power—The average power scheduled to be exchanged during a given schedule interval (example units: kW).

6.4 Appendix D Example Formulation of Distributed Relative Power Flow Introduction

Distributed control typically uses tools to assess effects of actions by distributed calculation. The challenge has been to predict power flow to and from neighbor nodes. When generation or loads change at the present node, it may be impossible to allocate such change among the power flow to and from neighbors without global knowledge.

Additionally, embodiments discussed herein might have ramifications for even centralized solvers as possible solution accelerator. Further, parallel calculations are enabled and global management of power angle becomes unnecessary.

Further, some embodiments exhibit iterative improvement of the solution occurs over time.

Discussion

The example method introduced below is formulated for distributed transactive control, where decisions are made independently at distributed locations to respond to an incentive signal. The impacts of these decisions on power flow are desirably predicted, which is presently challenging to do with conventional power flow formulations.

The example method is “relative” in that the objective of a node is to locate itself among neighbor nodes while assuming that the vector positions of those nodes do not change during an iteration. In this example, each node considers its own vector state location to be its system reference.

A node does not necessarily have to know its neighbor's state. In fact, there is not necessarily any system reference by which a node could make such an assessment. The relative vector state of a neighbor may be adequately inferred by receiving from that neighbor its anticipated complex power flow between it and the present node. It is not necessary even that the neighbors perfectly agree on the impedance of the transmission corridor between them.

A node's performance using this example method may be configured to improve over time with learning. Eventually, a node is able to test its prediction as the predicted time (or interval) occurs and passes.

The method is an embodiment of a Newton-Raphson relaxation method. The number of iterations of this method versus conventional power flow approaches will vary from implementation to implementation. Overrelaxation and other acceleration methods may be applicable. The power error can be used to assess status of the solution, or can assess ongoing dynamic system flux where the process is allowed to track updates to predicted states in “real time.”

Example Embodiment

1. Receive neighbors' predicted real and reactive flow estimates for iteration k. Power P_(0,n) is to be exported to neighboring node n; reactive power Q_(0,n) is to be exported to neighboring node n. The basic node equations that will be used in the formulation are:

$\begin{matrix} {P_{0} = {{P_{0,{Gen}} - P_{0,{Load}}} = {\sum\limits_{n = 1}^{\infty}P_{0,n}}}} & {{Eq}.\mspace{14mu} 1} \\ {Q_{0} = {{Q_{0,{Gen}} - Q_{0,{Load}}} = {\sum\limits_{n = 1}^{N}Q_{0,n}}}} & {{Eq}.\mspace{14mu} 2} \end{matrix}$

2. Calculate the real and reactive power errors based on neighbors' estimated real and reactive power exchange that they have provided and any updated estimates of generation and load at this node:

$\begin{matrix} {{\Delta \; P}\overset{.}{=}{P_{0,{Gen}} - P_{0,{Load}} - {\sum\limits_{n = 1}^{\infty}P_{0,n}}}} & {{Eq}.\mspace{14mu} 3} \\ {{\Delta \; Q}\overset{.}{=}{Q_{0,{Gen}} - Q_{0,{Load}} - {\sum\limits_{n = 1}^{N}Q_{0,n}}}} & {{Eq}.\mspace{14mu} 4} \end{matrix}$

3. Use real and reactive flow and knowledge of corridor impedance to solve for and update the voltages and relative angles of each neighbor. Use the best present estimate of this node's voltage V₀ for this iteration k and the power P₀, and reactive power Q_(0,n) reported by interacting neighbor nodes. Note that the voltages and power angles of neighboring nodes are inferred from their reports of how much real and reactive power they intend to import or export. Neighbors need not perfectly agree on their relative voltages and angles in order for this approach to work. As derived in the appendix:

$\begin{matrix} {V_{n} = \frac{\left( {V_{0} - \frac{Q_{0,n}X_{0,n}}{V_{0}}} \right)}{\cos \left( {\tan^{- 1}\left( \frac{P_{0,n}X_{0,n}}{V_{0}^{2} - {Q_{0,n}X_{0,n}}} \right)} \right)}} & {{Eq}.\mspace{14mu} 5} \\ {{\delta_{0} - \delta_{n}} = {\tan^{- 1}\left( \frac{P_{0,n}X_{0,n}}{V_{0}^{2} - {Q_{0,n}X_{0,n}}} \right)}} & {{Eq}.\mspace{14mu} 6} \end{matrix}$

4. Update Jacobian elements for this node's voltage and angle using the updated state variables from this iteration k. The state variables are the relative angles between this node and its neighbors, the voltages of neighbor nodes, and the voltage of this node. For this formulation, one can assume values of δ_(n) and V_(n) are held constant during the iteration. Such differentials can be calculated that will allow expansion of the power errors in terms of the voltage and angle of this node only, as will be accomplished in the next steps.

$\begin{matrix} {\frac{P}{V_{0}} = {\sum\limits_{n = 1}^{N}{\frac{V_{n}}{X_{0,n}}{\sin \left( {\delta_{0} - \delta_{n}} \right)}}}} & {{Eq}.\mspace{14mu} 7} \\ {\frac{P}{\delta_{0}} = {\sum\limits_{n = 1}^{N}{\frac{V_{0}V_{n}}{X_{0,n}}{\cos \left( {\delta_{0} - \delta_{n}} \right)}}}} & {{Eq}.\mspace{14mu} 8} \\ {\frac{Q}{V_{0}} = {\sum\limits_{n = 1}^{N}{\frac{1}{X_{0,n}}\left\lbrack {{2V_{0}} - {V_{n}{\cos \left( {\delta_{0} - \delta_{n}} \right)}}} \right\rbrack}}} & {{Eq}.\mspace{14mu} 9} \\ {\frac{Q}{\delta_{0}} = {\sum\limits_{n = 1}^{N}{\frac{V_{0}V_{n}}{X_{0,n}}{\sin \left( {\delta_{0} - \delta_{n}} \right)}}}} & {{Eq}.\mspace{14mu} 10} \end{matrix}$

-   -   Derivations of Eqs. 7-8 can be found in the appendix. Note that         the values calculated for Eq. 8 and Eq. 9 are much larger and         more influential than those calculated in Eq. 7 and Eq. 10.         Consequently, the calculation can be accelerated by using only         these two components, thus decoupling the real and reactive         components of power flow. Alternatively, the system can be         established to manage only real power or only reactive power         (separate control mechanisms).         5. Calculate voltage change and angle change of this node only.         These two unknowns are solvable from power and reactive power         equations and a first linear expansion with respect to changes         in the voltage ΔV₀ and angle Δδ₀. Because this is a relative         formulation, a solution is found for the new conditions of this         node that will solve the real and reactive power errors. The         result will be an updated voltage for this node. The angle will         later be discarded and is not a state (the angle of this node is         defined as the reference), but the resulting angle help us         allocate changes in power flow among the powers being exchanged         with neighbors.

$\begin{matrix} {{\Delta \; P} = {{\sum\limits_{n = 1}^{N}{\frac{P_{0,n}}{\delta_{0}} \cdot {\Delta\delta}_{0}}} + {\sum\limits_{n = 1}^{N}{{\frac{P_{0,n}}{V_{0}} \cdot \Delta}\; V_{0}}}}} & {{Eq}.\mspace{14mu} 11} \end{matrix}$

-   -   By substitution:

$\begin{matrix} {{\Delta \; P} = {{\sum\limits_{n = 1}^{N}{\frac{V_{0}V_{n}}{X_{0,n}}{{\cos \left( {\delta_{0} - \delta_{n}} \right)} \cdot {\Delta\delta}_{0}}}} + {\sum\limits_{n = 1}^{N}{\frac{V_{n}}{X_{0,n}}{{\sin \left( {\delta_{0} - \delta_{n}} \right)} \cdot \Delta}\; V_{0}}}}} & {{Eq}.\mspace{14mu} 12} \\ {\mspace{79mu} {{\Delta \; Q} = {{\sum\limits_{n = 1}^{N}{\frac{Q_{0,n}}{\delta_{0}} \cdot {\Delta\delta}_{0}}} + {\sum\limits_{n = 1}^{N}{{\frac{Q_{0,n}}{V_{0}} \cdot \Delta}\; V_{0}}}}}} & {{Eq}.\mspace{14mu} 13} \end{matrix}$

-   -   By substitution:

$\begin{matrix} {{\Delta \; Q} = {{\sum\limits_{n = 1}^{N}{\frac{V_{0}V_{n}}{X_{0,n}}{{\sin \left( {\delta_{0} - \delta_{n}} \right)} \cdot {\Delta\delta}_{0}}}} + {\sum\limits_{n = 1}^{N}{{{\frac{1}{X_{0,n}}\left\lbrack {{2V_{0}} - {V_{n}{\cos \left( {\delta_{0} - \delta_{n}} \right)}}} \right\rbrack} \cdot \Delta}\; V_{0}}}}} & {{Eq}.\mspace{14mu} 14} \end{matrix}$

-   -   Finish updating the state variables at this node using the         changes that were calculated using Eq. 12 and Eq. 14.

δ₀(k+1)=δ₀(k)+Δδ₀  Eq. 15

V ₀(k+1)=V ₀(k)+ΔV ₀  Eq. 16

6. Use the updated voltage state and temporary angle for this node to calculate refine the estimate of real and reactive power to be exchanged with neighbors. (The change in angle may be used to modify the relative angle states, but doing so is not necessary.)

$\begin{matrix} {\mspace{79mu} {{P_{0,n}\left( {k + 1} \right)} = {\frac{{V_{0}\left( {k + 1} \right)}{V_{n}(k)}}{X_{0,n}}{\sin \left( {{\delta_{0}\left( {k + 1} \right)} - {\delta_{n}(k)}} \right)}}}} & {{Eq}.\mspace{14mu} 17} \\ {{Q_{0,n}\left( {k + 1} \right)} = {\frac{V_{0}\left( {k + 1} \right)}{X_{0,n}}\left\lbrack {{V_{0}\left( {k + 1} \right)} - {{V_{n}(k)}{\cos \left( {{\delta_{0}\left( {k + 1} \right)} - {\delta_{n}(k)}} \right)}}} \right\rbrack}} & {{Eq}.\mspace{14mu} 18} \end{matrix}$

7. Provide these updated estimates of real and reactive power to be exchanged with neighbors to those neighbors for their use with iteration k+1, (the values calculated in step 6 are those that will be shared with neighbors during iteration k+1.) 8. Reset this node's angle to zero.

δ₀=0  Eq. 19

9. Calculate the real and reactive power errors given the updated state. This power error may be used for confidence assessments and convergence criteria. (See steps 4 and 5.)

$\begin{matrix} {{\Delta \; P} = {{P_{0,{Gen}}\left( {k + 1} \right)} - {P_{0,{Load}}\left( {k + 1} \right)} - {\sum\limits_{n = 1}^{\infty}{P_{0,n}\left( {k + 1} \right)}}}} & {{Eq}.\mspace{14mu} 20} \\ {{\Delta \; Q} = {{Q_{0,{Gen}}\left( {k + 1} \right)} - {Q_{0,{Load}}\left( {k + 1} \right)} - {\sum\limits_{n = 1}^{N}{Q_{0,n}\left( {k + 1} \right)}}}} & {{Eq}.\mspace{14mu} 21} \end{matrix}$

10. Repeat. If the process is repeated using the same neighbors' estimates of real and reactive power, this node's voltage may be further resolved. If the process is repeated using newly updated neighbors' estimates of real and reactive power for iteration k+1, the entire system power flow solution becomes refined by iteration.

Examples

The approach can be demonstrated using a simple example where a node interacts with only two neighbors and must assess its relative power flow state from information reported by these two neighbors. Let this node have no real or reactive generation or load. One possible flow state having small power error is shown in diagram 9800 of FIG. 99.

In step 1, assume that a perturbation has occurred at node 2 and it reports 1.2+j1.0 should now be leaving the center node. Node 1 reports an unchanged complex power flow of 1.0+j1.0.

In step 2, the new power error is calculated to be −0.2 because there now appears to be 0.2 more real power leaving this node than entering it.

In step 3, the voltage and angle of node 2 is corrected to match the complex power that is reported to the present node by node 2. This is illustrated in diagram 9900 of FIG. 99.

In step 4, the present variability of power is assessed based on the state determined in step 3.

$\begin{matrix} {\frac{P}{\delta_{0}} = 19.9994} & {\frac{P}{V_{0}} = 0.1999} \\ {\frac{Q}{\delta_{0}} = 0.1999} & {\frac{Q}{V_{0}} = 20.0006} \end{matrix}$

In step 5, solve for the corresponding changes of this node's voltage and angle that will help resolve the power error. The voltage and angle of this node are updated accordingly.

Δδ₀=−0.0100 radians=0.573° ΔV ₀=0.0001

In step 6, real and reactive power to be exchanged with neighbor nodes is recalculated using the new voltage and angle for the present node. The implications of this calculation are shown in diagram 10000 of FIG. 100. The voltage and angle of the present node have been altered, which has changed also the real and reactive power that would be exchanged by this node with nodes 1 and 2. The resulting power is balanced partway between the powers that had been reported by nodes 1 and 2 at the beginning of the iteration. The reactive power is unfortunately decreased by about 1%, an outcome of the nonlinearity of the calculation.

Interestingly, the result of fast decoupled calculations at this node would have been resulted in about the same result.

APPENDIX

1. Real and reactive power flow between this and neighbor node: Apparent power:

S= VI*  Eq. A1

Voltage at this node is defined as V₀·e^(jδ) ⁰ . Current leaving this node is defined as

$\frac{{V_{0} \cdot ^{{j\delta}_{0}}} - {V_{n} \cdot ^{{j\delta}_{n}}}}{j^{X_{0,n}}},$

where a common practice has been adopted of representing the impedance between the nodes by the reactance component only.

By substitution of these values into Eq. AI,

$\begin{matrix} {\overset{\_}{S} = {j{{\frac{V_{0}}{X_{0,n}}\left\lbrack {V_{0} - {V_{n} \cdot ^{j{({\delta_{0} - \delta_{n}})}}}} \right\rbrack}.}}} & {{{Eq}.\mspace{14mu} A}\; 2} \end{matrix}$

Real power leaving this node to node n is the real part of the apparent power:

$\begin{matrix} {{P_{0,n} \equiv {{Re}\left\{ \overset{\_}{S} \right\}}} = {\frac{V_{0}V_{n}}{X_{0,n}}{\sin \left( {\delta_{0} - \delta_{n}} \right)}}} & {{Eq}.\mspace{14mu} {A3}} \end{matrix}$

Reactive power leaving this node to node n is the imaginary component of the apparent power:

$\begin{matrix} {{Q_{0,n} \equiv {{Im}\left\{ \overset{\_}{S} \right\}}} = {\frac{V_{0}}{X_{0,n}}\left\lbrack {V_{0} - {V_{n}{\cos \left( {\delta_{0} - \delta_{n}} \right)}}} \right\rbrack}} & {{Eq}.\mspace{14mu} {A4}} \end{matrix}$

2. Given power and reactive power, calculate neighbor's voltage and relative angle.

The reactive power equation can be used to solve for neighbor's voltage and relative angle. First solve Eq. A4 for V_(n) with respect to the relative angle.

$\begin{matrix} {V_{n} = \frac{\left( {V_{0} - \frac{Q_{0,n}X_{0,n}}{V_{0}}} \right)}{\cos \left( {\delta_{0} - \delta_{n}} \right)}} & {{Eq}.\mspace{14mu} {A5}} \end{matrix}$

By substitution of V_(n) into Eq. A3, the relative angle may be calculated in terms of known variables.

$\begin{matrix} {{\delta_{0} - \delta_{n}} = {\tan^{- 1}\left( \frac{P_{0,n}X_{0,n}}{V_{0}^{2} - {Q_{0,n}X_{0,n}}} \right)}} & {{Eq}.\mspace{14mu} {A6}} \end{matrix}$

And by substitution of the relative angle of Eq. A6 into Eq. A5,one can solve for V_(n) also in terms of known variables:

$\begin{matrix} {V_{n} = \frac{\left( {V_{0} - \frac{Q_{0,n}X_{0,n}}{V_{0}}} \right)}{\cos \left( {\tan^{- 1}\left( \frac{P_{0,n}X_{0,n}}{V_{0}^{2} - {Q_{0,n}X_{0,n}}} \right)} \right)}} & {{Eq}.\mspace{14mu} {A7}} \end{matrix}$

3. Jacobian sensitivities of power at this node to changes in this node's voltage and relative angles:

Differentiate Eq. A3 for every neighbor node n with respect to V₀ and with respect to the relative power angle δ₀−δ_(n) to get Eq. A8 and Eq. A9:

$\begin{matrix} {\frac{P_{0,n}}{V_{0}} = {\sum\limits_{n = 1}^{N}\frac{V_{n}}{X_{0,n}{\sin \left( {\delta_{0} - \delta_{n}} \right)}}}} & {{Eq}.\mspace{14mu} {A8}} \\ {\frac{P_{0,n}}{\left( {\delta_{0} - \delta_{n}} \right)} = {\sum\limits_{n = 1}^{N}{\frac{V_{0}V_{n}}{X_{0,n}}{\cos \left( {\delta_{0} - \delta_{n}} \right)}}}} & {{Eq}.\mspace{14mu} {A9}} \end{matrix}$

This formulation will assume that δ_(n) remains constant through this iteration. Solving with respect to this node's angle,

$\begin{matrix} {\frac{P_{0,n}}{\delta_{0}} = {\sum\limits_{n = 1}^{N}{\frac{V_{0}V_{n}}{X_{0,n}}{{\cos \left( {\delta_{0} - \delta_{n}} \right)}.}}}} & {{Eq}.\mspace{14mu} {A10}} \end{matrix}$

4. Jacobian sensitivities of reactive power at this node to changes in this node's voltage and relative angles:

Similar to what was done above, differentiate Eq. A4 for every neighbor node n with respect to V₀ and with respect to the relative power angle δ₀−δ_(n) to get Eq. A11 and Eq. A12:

$\begin{matrix} {\frac{Q_{0,n}}{V_{0}} = {\sum\limits_{n = 1}^{N}{\frac{1}{X_{0,n}}\left\lbrack {{2V_{0}} - {V_{n}{\cos \left( {\delta_{0} - \delta_{n}} \right)}}} \right\rbrack}}} & {{Eq}.\mspace{14mu} {A11}} \\ {\frac{Q_{0,n}}{\left( {\delta_{0} - \delta_{n}} \right)} = {\sum\limits_{n = 1}^{N}{\frac{V_{0}V_{n}}{X_{0,n}}{\sin \left( {\delta_{0} - \delta_{n}} \right)}}}} & {{Eq}.\mspace{14mu} {A12}} \end{matrix}$

Remembering that δ_(n) remains constant through this iteration and solving with respect to this node's angle,

$\begin{matrix} {\frac{Q_{0,n}}{\delta_{0}} = {\sum\limits_{n = 1}^{N}{\frac{V_{0}V_{n}}{X_{0,n}}{{\sin \left( {\delta_{0} - \delta_{n}} \right)}.}}}} & {{Eq}.\mspace{14mu} {A13}} \end{matrix}$

5. Power and reactive power error definitions:

$\begin{matrix} {{\Delta \; P} = {P_{Gen} - P_{Load} - {\sum\limits_{n = 1}^{N}P_{0,n}}}} & {{Eq}.\mspace{14mu} {A14}} \\ {{\Delta \; Q} = {Q_{Gen} - Q_{load} - {\sum\limits_{n = 1}^{N}Q_{0,n}}}} & {{Eq}.\mspace{14mu} {A15}} \end{matrix}$

6. Calculate voltage and angle of this node.

In a traditional power flow calculation, linear expansion would be completed about all power angle and voltage states. For example,

$\begin{matrix} {{\Delta \; P} = {{\sum\limits_{n = 1}^{N}{\frac{P_{0,n}}{\left( {\delta_{0} - {\delta \; n}} \right)} \cdot {\Delta \left( {\delta_{n} - \delta_{0}} \right)}}} + {\sum\limits_{n = 1}^{N}{{\frac{P_{0,n}}{V_{0}} \cdot \Delta}\; V_{0}}} + {\sum\limits_{n = 1}^{N}{{\frac{P_{0,n}}{V_{n}} \cdot \Delta}\; {V_{n}.}}}}} & {{Eq}.\mspace{14mu} {A16}} \end{matrix}$

In the present, relative formulation, assume δ_(n) and V_(n) are constant through each iteration at this node. Eq. A16 can be simplified to

$\begin{matrix} {{\Delta \; P} = {{\sum\limits_{n = 1}^{N}{\frac{P_{0,n}}{\delta_{0}} \cdot {\Delta\delta}_{0}}} + {\sum\limits_{n = 1}^{N}{{\frac{P_{0,n}}{V_{0}} \cdot \Delta}\; {V_{0}.}}}}} & {{{Eq}.\mspace{14mu} A}\; 17} \end{matrix}$

Remembering Eq. A8 and Eq. A1D, by substitution,

$\begin{matrix} {{\Delta \; P} = {{\sum\limits_{n = 1}^{N}{\frac{V_{0}V_{n}}{X_{0,n}}{{\cos \left( {\delta_{0} - \delta_{n}} \right)} \cdot {\Delta\delta}_{0}}}} + {\sum\limits_{n = 1}^{N}{\frac{V_{n}}{X_{0,n}}{{\sin \left( {\delta_{0} - \delta_{n}} \right)} \cdot \Delta}\; {V_{0}.}}}}} & {{Eq}.\mspace{14mu} {A18}} \end{matrix}$

Similarly, for Q, a traditional linearization might result in

$\begin{matrix} {{\Delta \; Q} = {{\sum\limits_{n = 1}^{N}{\frac{Q_{0,n}}{\left( {\delta_{0} - \delta_{n}} \right)} \cdot {\Delta \left( {\delta_{0} - \delta_{n}} \right)}}} + {\sum\limits_{n = 1}^{N}{{\frac{Q_{0,n}}{V_{0}} \cdot \Delta}\; V_{0}}} + {\sum\limits_{n = 1}^{N}{{\frac{Q_{0,n}}{V_{n}} \cdot \Delta}\; {V_{n}.}}}}} & {{Eq}.\mspace{14mu} {A19}} \end{matrix}$

In the present, relative formulation, assume δ_(n) and V_(n) are constant through each iteration at this node. Eq. A19 can be simplified to

$\begin{matrix} {{\Delta \; Q} = {{\sum\limits_{n = 1}^{N}{\frac{Q_{0,n}}{\delta_{0}} \cdot {\Delta\delta}_{0}}} + {\sum\limits_{n = 1}^{N}{\frac{Q_{0,n}}{V_{0}} \cdot {V_{0}.}}}}} & {{Eq}.\mspace{14mu} {A20}} \end{matrix}$

Remembering Eq. A11 and Eq. A13, by substitution,

$\begin{matrix} {{\Delta \; Q} = {{\sum\limits_{n = 1}^{N}{\frac{V_{0}V_{n}}{X_{0,n}}{{\sin \left( {\delta_{0} - \delta_{n}} \right)} \cdot {\Delta\delta}_{0}}}} + {\sum\limits_{n = 1}^{N}{{{\frac{1}{X_{0,n}}\left\lbrack {{2V_{0}} - {V_{n}{\cos\left\lbrack {\delta_{0} - \delta_{n}} \right)}}} \right\rbrack} \cdot \Delta}\; {V_{0}.}}}}} & {{Eq}.\mspace{14mu} {A21}} \end{matrix}$

7 Concluding Remarks

Having illustrated and described the principles of the disclosed technology, it will be apparent to those skilled in the art that the disclosed embodiments can be modified in arrangement and detail without departing from such principles. For example, any one or more aspects of the disclosed technology can be applied in other embodiments. In view of the many possible embodiments to which the principles of the disclosed technologies can be applied, it should be recognized that the illustrated embodiments are only preferred examples of the technologies and should not be taken as limiting the scope of the invention. 

What is claimed is:
 1. A method for operating a transactive node in a market-based electrical-energy-allocation system, comprising: by computing hardware: computing incentive signal data, the incentive signal data comprising data indicative of a cost of electric energy at the transactive node at a current time interval and data indicative of a forecasted cost of electric energy at the transactive node at one or more future time intervals; computing feedback signal data, the feedback signal data comprising data indicative of an electric load at the transactive node at the current time interval and data indicative of a forecasted load for electric energy at the transactive node at the one or more future time intervals; and transmitting the incentive signal data and the feedback signal data.
 2. The method of claim 1), wherein the data indicative of the cost of electric energy comprises data indicative of a cost of real electrical energy, reactive electrical energy, or a combination of both real and reactive electrical energies at the transactive node at the current time interval, and wherein the data indicative of the forecasted cost of electric energy comprises data indicative of a forecasted cost of real electrical energy, reactive electrical energy, or a combination of both real and reactive electrical energies at the transactive node at the one or more future time intervals.
 3. The method of claim 1), wherein the data indicative of the electric load comprises data indicative of a real electrical load, reactive electrical load, or a combination of both real and reactive electrical loads at the transactive node at the current time interval, and wherein the data indicative of the forecasted load for electric energy comprises data indicative of a forecasted load of real electrical load, reactive electrical load, or a combination of both real and reactive electrical loads at the transactive node at the one or more future time intervals.
 4. The method of claim 1), wherein the incentive signal data further comprises data indicating a confidence level that the data indicative of the cost of electric energy at the transactive node at the current time interval is accurate, and data indicating a confidence level that the data indicative of the forecasted cost of electric energy at the transactive node at the one or more future time intervals is accurate.
 5. The method of claim 1), wherein the feedback signal data further comprises data indicating a confidence level that the data indicative of the electric load at the transactive node at the current time interval is accurate, and data indicating a confidence level that the data indicative of the forecasted load for electric energy at the transactive node at the one or more future time intervals is accurate.
 6. The method of claim 1), wherein the method further comprises receiving incentive signal data and feedback signal data from one or more neighboring transactive nodes, wherein the computing the incentive signal data is based at least in part on the received incentive signal data, and wherein the computing the feedback signal data is based at least in part on the received feedback signal data.
 7. One or more non-transitory computer-readable media storing computer-readable instructions for causing computer to perform the method of claim 1).
 8. A transactive node comprising computing hardware configured to perform the method of claim 1).
 9. A method for operating a transactive node in a market-based electrical-energy-allocation system, comprising: by computing hardware: receiving incentive signal data at the transactive node from two or more neighboring transactive nodes, the incentive signal data from the two or more neighboring transactive nodes comprising data indicative of at least a cost of electric energy at a current time interval; computing aggregated incentive signal data based at least in part on the incentive signal data from the two or more neighboring transactive nodes; and transmitting the aggregated incentive signal data to a further transactive node
 10. The method of claim 9), wherein the received incentive signal data and the transmitted aggregated incentive signal data comprise data indicative of a cost of real electrical energy, reactive electrical energy, or a combination of both real and reactive electrical energies.
 11. The method of claim 9), wherein the received incentive signal data further includes data indicating a confidence level of the received incentive signal data.
 12. The method of claim 9), wherein the transmitted incentive signal data further includes data indicating a confidence level of the transmitted incentive signal data.
 13. The method of claim 9), wherein the aggregated incentive signal data comprises a weighted sum of the incentive signal data from the two or more neighboring transactive nodes.
 14. The method of claim 9), wherein the aggregated incentive signal data is further modified to provide an incentive or disincentive to the further transactive node based on local conditions at the transactive node.
 15. The method of claim 9), wherein the received incentive signal data comprises data indicative of the cost of electric energy at the current time interval and data indicative of a forecasted cost of electric energy at one or more future time intervals, and wherein the aggregated incentive signal data comprises data indicative of the aggregated cost of electric energy at the current time interval and data indicative of a forecasted aggregated cost of electric energy at one or more future time intervals.
 16. The method of claim 9), wherein the method further comprises: receiving feedback signal data at the transactive node from the two or more neighboring transactive nodes, the feedback signal data from the two or more neighboring transactive nodes comprising data indicative of at least an electric load for electric energy at a current time interval; computing aggregated feedback signal data based at least in part on the feedback signal data from the two or more neighboring transactive nodes; and transmitting the aggregated feedback signal data to the further transactive node.
 17. The method of claim 16), wherein the received feedback signal data comprises data indicative of the electric load for electric energy at the current time interval and data indicative of a forecasted load of electric energy at the one or more future time intervals, and wherein the aggregated feedback signal data comprises data indicative of the aggregated load of electric energy at the current time interval and data indicative of a forecasted aggregated load of electric energy at one or more future time intervals.
 18. One or more non-transitory computer-readable media storing computer-readable instructions for causing computer to perform the method of claim 9).
 19. A transactive node comprising computing hardware configured to perform the method of claim 9).
 20. A method for operating a transactive node in a market-based electrical-energy-allocation system, comprising: by computing hardware: receiving feedback signal data at a transactive node from two or more neighboring transactive nodes, the feedback signal data from the two or more neighboring transactive nodes comprising data indicative of at least an electric load for electric energy at a current time interval; computing aggregated feedback signal data based at least in part on the feedback signal data from the two or more neighboring transactive nodes; and transmitting the aggregated feedback signal data to a further transactive node.
 21. The method of claim 20), wherein the received feedback signal data and the transmitted aggregated feedback signal data comprise data indicative of a real electrical load, reactive electrical load, or a combination of both real and reactive electrical loads.
 22. The method of claim 20), wherein the received feedback signal data further includes data indicating a confidence level of the received feedback signal data.
 23. The method of claim 20), wherein the transmitted feedback signal data further includes data indicating a confidence level of the transmitted feedback signal data.
 24. The method of claim 20), wherein the received feedback signal data comprises data indicative of the electric load of electric energy at the current time interval and data indicative of a forecasted load of electric energy at one or more future time intervals, and wherein the aggregated feedback signal data comprises data indicative of the aggregated load of electric energy at the current time interval and data indicative of a forecasted aggregated load of electric energy at the one or more future time intervals.
 25. The method of claim 20), wherein the method further comprises: receiving incentive signal data at the transactive node from the two or more neighboring transactive nodes, the incentive signal data from the two or more neighboring transactive nodes comprising data indicative of at least a cost of electric energy at the current time interval; computing aggregated incentive signal data based at least in part on the incentive signal data from the two or more neighboring transactive nodes; and transmitting the aggregated incentive signal data to the further transactive node.
 26. The method of claim 25), wherein the received incentive signal data comprises data indicative of the cost of electric energy at the current time interval and data indicative of a forecasted cost of electric energy at the one or more future time intervals, and wherein the aggregated incentive signal data comprises data indicative of the aggregated cost of electric energy at the current time interval and data indicative of a forecasted aggregated cost of electric energy at one or more future time intervals.
 27. One or more non-transitory computer-readable media storing computer-readable instructions for causing computer to perform the method of claim 20).
 28. A transactive node comprising computing hardware configured to perform the method of claim 20).
 29. A system for distributing electricity, comprising: a plurality of transactive nodes, each transactive node being associated with one or more electric resources, one or more electric loads, or a combination of one or more electric resources and one or more electric loads; and a network connected to the transactive nodes to facilitate communication between the transactive nodes, the transactive nodes being configured to exchange incentive and feedback signals with one another in order to determine an electrical demand in the system for a current time interval and to provide an electrical supply sufficient to meet the electrical demand for the current time interval.
 30. The system of claim 29), wherein the current time interval is the time interval that is next to occur and be coordinated by the system.
 31. The system of claim 29), wherein the transactive nodes are further configured to exchange incentive and feedback signals for two or more future time intervals in addition to the incentive and feedback signals for the current time interval.
 32. The system of claim 31), wherein the two or more future time intervals have increasingly coarser granularity.
 33. The system of claim 29), wherein at least one of the transactive nodes modifies one or both of its incentive or feedback signals in response to previously received incentive and feedback signals.
 34. The system of claim 33), wherein the at least one of the transactive nodes is associated with an elastic load, and wherein the modified incentive or feedback signals corresponds to a predicted change in the elastic load.
 35. The system of claim 33), wherein the at least one of the transactive nodes is associated with an electrical resource, and wherein the modified incentive or feedback signals corresponds to a change in the electrical resource.
 36. The system of claim 33), wherein the at least one of the transactive nodes is associated with an electrical resource, and wherein the modified incentive signals correspond to a change in local conditions.
 37. The system of claim 29), wherein one or more of the transactive nodes compute their respective incentive and feedback signals using functions selected from a library of functions.
 38. The system of claim 29), wherein the incentive and feedback signals further include confidence level data indicating a respective reliability of the incentive and feedback signals.
 39. A system for distributing electricity, comprising: a plurality of transactive nodes, each transactive node being associated with one or more electric resources, one or more electric loads, or a combination of one or more electric resources and one or more electric loads; and a network connected to the transactive nodes and facilitating communication between the transactive nodes, the transactive nodes being configured to exchange sets of signals with one another in order to determine an electrical demand in the system for a current time interval and to provide an electrical supply sufficient to meet the electrical demand for the current time interval, each set of signals including signals for determining the electric loads and supplies for the current time interval as well as signals for determining the electric loads and supplies for two or more future time intervals.
 40. The system of claim 39), wherein the current time interval corresponds to an imminent time interval that is next to occur in the system.
 41. The system of claim 39), wherein the future time intervals have increasingly longer durations as the time intervals are farther into the future relative to the current time interval.
 42. The system of claim 39), wherein the transactive nodes are configured to update the values of the sets of signals at an update frequency, the update frequency corresponding to a duration of the current time interval.
 43. The system of claim 39), wherein the transactive nodes are configured to exchange the set of signals with one another iteratively over time such that the signals for a respective time interval stabilize as the respective time interval approaches the current time interval.
 44. The system of claim 39), wherein the transactive nodes are configured to exchange the set of signals with one another on an asynchronous event-driven basis or a clock-driven basis.
 45. The system of claim 39), wherein a respective set of the transactive nodes are configured to iteratively exchange a set of signals with one another until the exchanged set of signals converges to within an acceptable degree of tolerance.
 46. The system of claim 39), wherein a transactive node in the respective set of the transactive nodes is further configured to transmit an updated set of signals when local conditions at the transactive node cause the updated set of signals to deviate from a previously transmitted set of signals beyond a relaxation criterion.
 47. The system of claim 39), wherein the sets of signals further include confidence level data indicating a respective reliability of the exchanged signals.
 48. A system for distributing electricity, comprising: a plurality of transactive nodes, each transactive node being associated with one or more electric supplies, one or more electric loads, or a combination of one or more electric supplies and one or more electric loads; and a network connected to the transactive nodes and facilitating communication between the transactive nodes, the transactive nodes being configured to exchange sets of signals with one another in order to determine an electrical demand in the system for a current time interval and to provide an electrical supply sufficient to meet the electrical demand for the current time interval, a respective one of the transactive nodes being configured to compute its incentive and feedback signals using one or more functions selected from a library of functions.
 49. The system of claim 48), wherein the one or more functions selected from the library of functions are selected based on the type and number of electrical supplies and electrical loads with which the respective transactive node is associated.
 50. The system of claim 48), wherein the one or more functions include one or more load functions, one or more resource functions, or a combination of one or more load functions and resource functions.
 51. The system of claim 48), wherein the one or more functions are selected from a group of resource functions comprising one or more of: (a. a resource function adapted to account for imported electrical energy, (b. a resource function adapted to account for a renewable energy resource, (c. a resource function adapted to account for fossil fuel generation; (d. a resource function adapted to account for general infrastructure cost; (e. a resource function adapted to account for system constraints; (f. a resource function adapted to account for system energy losses; (g. a resource function adapted to account for demand charges; (h. a resource function adapted to account for market impacts.
 52. The system of claim 48), wherein the one or more functions are selected from a group of load functions comprising one or more of: (a. a load function adapted to account for a bulk inelastic load, (b. a load function adapted to account for an event-driven demand response; (c. a load function adapted to account for a time-of-use demand response; (d. a load function adapted to account for a real-time continuum demand response.
 53. The system of claim 48), wherein the respective one of the transactive nodes controls one or more elastic loads and adjusts the one or more elastic loads in response to the feedback and incentive signals received at the respective one of the transactive nodes.
 54. The system of claim 48), wherein the one or more functions are implemented by individual software modules that can be combined with one another to implement the desired transactor behavior for the respective one of the transactive nodes.
 55. The system of claim 48), wherein, through the use of the one or more functions, the respective one of the transactive nodes computes a control signal selected from a set of signed whole numbers and communicates the computed control signal to one or more loads, resources, or loads and resources associated with the respective one of the transactive nodes.
 56. The system of claim 55), wherein the computed control signal is interpreted by an electrical generator or set of electrical generators as a fraction of the generator's or generators' rated generation capacity.
 57. The system of claim 55), wherein the computed control signal is interpreted by an electrical load or set of electrical loads as a fraction of the load's or loads' rated power.
 58. A method, comprising: for a transactive node associated with one or more electric resources, one or more electric loads, or a combination of one or more electric resources or one or more electric loads; selecting one or more functions from a library of functions, the selection being based at least in part on the type of one or more electric resources or electric loads associated with the transactive node; and configuring the transactive node to compute transactive signals using the selected one or more functions.
 59. The method of claim 58), wherein the selected one or more functions are adapted for the type of electrical load or electrical supply associated with the transactive node.
 60. The method of claim 58), wherein the configuring comprising causing computing hardware used to implement the transactive node to execute a software program for performing computations using the selected one or more functions.
 61. The method of claim 58), further comprising accessing a database storing the library of functions.
 62. The method of claim 61), wherein the database is a database remotely located from the transactive node.
 63. The method of claim 58), wherein the selected one or more functions include a function that computes data representing one or more of energy, an energy cost, or an incentive for one or more electric resources associated with the transactive node.
 64. The method of claim 58), wherein the selected one or more functions include a function that computes data representing one or more of a predicted inelastic load or changes in elastic load for one or more electric loads associated with the transactive node.
 65. One or more non-transitory computer-readable media storing computer-readable instructions for causing computer to perform the method of claim 58).
 66. A transactive node comprising computing hardware configured to perform the method of claim 58). 